bluuebunny/crossref_metadata_2025 huggingface dataset:
Disentangling boredom from depression using the phenomenology and content of involuntary autobiographical memories
Authors: Yeung Ryan C. , Danckert James , Tilburg Wijnand A. P. Van , Fernandes Myra A.
Published: August 2023

Recurrent involuntary autobiographical memories (IAMs) are memories retrieved unintentionally and repetitively. We examined whether the phenomenology and content of IAMs could differentiate boredom and depression, both of which are characterized by affective dysregulation and spontaneous thought. Participants (n = 2,484) described their most frequent IAM and rated its phenomenological properties (e.g., valence). Structural topic modeling, a method of unsupervised machine learning, identified cohesive content within the described memories. Boredom proneness was positively correlated with depressive symptoms, and both were correlated with more negative recurrent IAMs. Boredom proneness predicted less vivid recurrent IAMs, whereas depressive symptoms predicted more vivid, negative, and emotionally intense ones. Memory content also diverged; topics such as relationship conflicts were positively predicted by depressive symptoms, but negatively predicted by boredom proneness. Phenomenology and content in recurrent IAMs can effectively disambiguate boredom proneness from depression in a large sample of undergraduate students from a racially diverse university.
Fetal Cortical Development and Neurosonographic Findings in Obese Pregnant Women: A Case Control Study from a Tertiary Hospital
Authors: Aktaş Betül Akgün , Kaya Ecem , Haksever Murat , Ozcan Gökce Kılınc , Kara Ozgür , Tanacan Atakan , Sahin Dilek
Published: August 2023

Purpose
To investigate the effect of obesity on fetal cortical development
Methods
This prospective cross-sectional study was conducted with 91 pregnant women. Fetal neurosonography scans were performed in the third trimester, and according to body mass index (BMI) values, the patients were evaluated in two groups: obese (BMI ≥ 30kg/m²) and normal weight (BMI < 30kg/m²). During neurosonography, fetal insular depth and Sylvian fissures, parieo-occipital and cingulate fissure depth, frontal lobe length, and the sizes of the corpus callosum and cavum septum pellucidum were measured using a transvaginal approach. Fetal cortical development and Sylvian fissure operculization were graded.
Results
The number of patients with grade 2 fetal cortical development was significantly higher among the pregnant women in the obese group compared to the normal weight group (n = 17, 41.5% and n = 8, 16.0%, respectively; p = 0.007). In the obese group, the number of pregnant women with grade 4 or below fetal Sylvian fissure operculization was significantly higher (n = 13, 31.7%), and the number of those with grade 9 or above operculization was significantly lower (n = 1, 2.5%) (p = 0.003). The fetal insular depth, frontal lobe anterior-posterior diameter, cingulate fissure depth, and corpus callosum thickness were lower in the obese group, albeit with no significant difference. Parieto-occipital depth significantly decreased in the obese group (7.4 ± 3.5 mm) compared to the control group (9.8 ± 4.3 mm) (p = 0.008). The fetal Sylvian fissure ratio and the cavum septum pellucidum ratio were found to be similar between the groups.
Conclusion
The data obtained from this study showed that obesity caused fetal cortical changes in pregnant women.
Allometric Equations for Estimating Above-Ground Biomass Carbon sequestration in Five Tree Species grown in an Intercropping Agroforestry System in Southern Ontario, Canada
Authors: Bazrgar Amir Behzad , Thevathasan Naresh , Gordon Andrew , Simpson Jamie
Published: August 2023

Allometric equations were developed for estimating above-ground biomass carbon (AGBC) sequestration in five tree species grown in a tree-based intercropping system at the University of Guelph Agroforestry Research Station (GARS), Guelph, Ontario, Canada. A total of 66 representative trees from five species: Red Oak (Quercus rubra) [n = 12], Black Walnut (Juglans nigra) [n = 16], Black Locust (Robinia pseudoacacia) [n = 10], White Ash (Fraxinus americana) [n = 15], Norway Spruce (Picea abies) [n = 13] were selected, harvested and their aboveground biomass and C content were quantified. Three commonly used allometric models were used to develop predictive equations. Regression models were developed and parameterized for each tree species and the best are presented based on information criteria (AIC, AICc, and BIC), mean absolute percentage error (MAPE), over/under estimation (MOUE), root mean square error (RMSE), R2, and regression coefficients (a, b) of the observed/predicted (OP) linear regression analysis. All equations with diameter at breast height (D) only and D and tree height (H) as the predictor variables fitted the AGBC data well, with R2 > 97% and RMSE < 40. However, a power model using D as the only predictor is recommended as the best model for Black Walnut, Black Locust, White Ash, and Norway Spruce. The models presented are the best fitted allometric equations for the indicated species and are recommended for these species, growing on similar soils under the same temperate conditions at densities of < 100 trees per hectare.
1H, 15N, 13C resonance assignments for proteasome shuttle factor hHR23a
Authors: Chen Xiang , Walters Kylie
Published: August 2023

hHR23a (human homolog of Rad23 a) functions in nucleotide excision repair and proteasome-mediated protein degradation. It contains an N-terminal ubiquitin-like (UBL) domain, an xeroderma pigmentosum C (XPC)-binding domain, and a ubiquitin-associated (UBA) domain preceding and following the XPC-binding domain. Each of the four structural domains are connected by flexible linker regions. We report in this NMR study, the 1H, 15N and 13C resonance assignments for the backbone and sidechain atoms of the hHR23a full-length protein with BioMagResBank accession number 52059. Assignments are 97% and 87% for the backbone (NH, N, C’, Cα, and Hα) and sidechain atoms of the hHR23a structured regions. The secondary structural elements predicted from the NMR data fit well to the hHR23a NMR structure. The assignments described in this manuscript can be used to apply NMR for studies of hHR23a with its binding partners.
Using network analysis to examine the connectivity between the brain regions in rs-fMRI data of FND patient and healthy participant : A single subject study
Authors: Ahmadi Samira , Ahmadi Farzaneh , Oghabian Mohammad Ali , Faghihzadeh Elham
Published: August 2023

Introduction: Functional neurological disorders (FND) is one of the most common causes of neuropathy, However, its cause continues to be mysterious. Understanding the underlying mechanisms of FND is crucial for treatment strategies. The study was conducted on brain images(rs-fMRI) taken from two volunteers (FND patient and healthy subject) who had the same characteristics.
Method: We fitted Gaussian Graphical Models to a single subject data using network approach.
Results: Based on the results of the networks, the number of significant edges was more in the left hemisphere in the patient, but in the healthy person, the number of these non-zero edges was more in the right hemisphere. Both the networks related to the healthy person and the patient had high density. Therefore, it indicated that the regions considered by these 2 people were strongly related to each other. The results showed the existence of more links and positive relationships between the regions, most of which showed a strong relationship. Among these connections, there were also negative connections. The networks of the healthy participant with almost symmetrical structures and the patient with FND showed different characteristics, including asymmetry between the hemispheres.
Conclusion: this study is the first to demonstrate that the brain regions of both FND patient and healthy participant can be conceptualized as networks. The findings of this study add to a growing body of literature that FND patient brain regions can be analyzed using network approaches.
Preparation of Au nanoparticles via anodic stripping of underpotential deposition-Cu in bulk electrodeposition-Au for high-performance electrochemical sensing of bisphenol A
Authors: Huang Zhao , Chen Zihan , Yan Dexuan , Jiang Shuo , Nie Libo , Tu Xinman , Chao Long
Published: August 2023

Bisphenol A is one of the most widely used industrial compound, has been intense concern over the years as its potential hazard to the human endocrine system and environment. This work reported a new bisphenol A electrochemical sensor prepared by convenient and controllable Cu-underpotential/Au-bulk co-electrodeposition on a glassy carbon electrode (GCE) modified with multiwalled carbon nanotubes, followed by electrochemical anodic stripping of Cu of underpotential deposition (UPD) and then yields a Au nanoparticles/multiwalled carbon nanotubes composite electrocatalyst modified electrode (AuCu-UPD/MWCNTs/GCE). The AuCu-UPD/MWCNTs/GCE has high electrocatalytic and high-performance sensing for bisphenol A (BPA). Under optimal conditions, the modified elctrode showed a two segments linear response from 0.01 to 1 µM and 1 to 20 µM with a limit of detection (LOD) of 2.43 nM based on differential pulse voltammetry (DPV). Determination of bisphenol A in real water samples on the AuCu-UPD/MWCNTs/GCE yields satisfactory results. The proposed electrochemical sensor can be promising for the development of simple, low-cost water quality monitoring system for detection of BPA in ambient water samples.
Universal Approximators from Anti-Derivatives: Enhancing Neural Networks
Authors: Lee Jeongsu
Published: August 2023

The existence of optimal neural networks, represented as combinations of piecewise functions, is proven by the universal approximation theorem. However, deriving this optimal solution from the training parameters of neural networks remains a challenging problem. This study proposes a novel strategy to construct an approximator for an arbitrary function, starting with a presumed optimal piecewise solution. The proposed approximation employs the anti-derivatives of a Fourier series expansion for the presumed piecewise function, leading to a remarkable feature that enables the simultaneous approximation of an arbitrary function and its anti-derivatives. Systematic experiments have demonstrated the outstanding merits of the proposed anti-derivatives-based approximator, such as the ability to solve differential equations and to enhance the capabilities of neural networks. Furthermore, the anti-derivatives approximator allows for the optimization of activation profiles within neural networks. This feature introduces a novel approach for finding unconventional activation profiles specialized for a given dataset.
Wigner's friend scenario, Born's rule and a new interpretation of quantum mechanics
Authors: Li Qian
Published: August 2023

In this paper, we analyze the thought experiment of "Wigner's friend" and point out that new understanding should be made to Born's rule and measurement process: Born's rule is no longer seen as a rule based on the history of the quantum system's, and the measurement results are no longer directly related to the state of the measured object before the measurement. Inspired by Everett III and H. Zurek's views, we believe that Born's rule reflects the coordination between different parts of the measured results in quantum entanglement systems, so it has nothing to do with the history of particles themselves but rather with the history records. A new interpretation of quantum mechanics is proposed, which is an objective interpretation. Under this interpretation, micro-particles can also be assigned definite states before being observed. Based on this interpretation, Wigner's friend-like scenarios or paradoxes extending Wigner's friend-like scenarios can be effectively explained. The new interpretation brings brand-new perspectives to many quantum phenomena. We also point out potential applications of the new interpretation in the future.
BLOCKCHAIN, CORPORATE STRUCTURE, AND FINANCIAL INTERMEDIATION
Authors: Arshadi Nasser
Published: August 2023

This paper reviews and synthesizes corporate finance and financial intermediation literature by highlighting transaction costs as a determining factor in their evolution. It then introduces blockchain as a potentially powerful technology that can significantly reduce transaction costs and therefore affect the structure of corporations and financial intermediaries. Modern corporations strive to coordinate functions internally to minimize transaction costs. And financial intermediaries attempt to resolve the information asymmetry problem among transacting parties. Over time, however, corporations and financial intermediation have been settled with the remaining incentive problems and their associated costs. For example, some financial intermediaries have committed fraud by timing their trades ahead of their customers, others by misquoting interest rates, and a few by fleecing customers by issuing unwanted credit cards. Regulators protecting customers often appear to be one step behind in preempting these intermediaries from wrongdoing. After introducing blockchain technology and explaining how it works, this paper examines the application of blockchain in real-estate finance, demonstrating how it can reduce or eliminate the role of multiple intermediaries in executing transactions.
Böcekler Üzerinde İklimdeki Değişimin Etkisi: Çayır tırtılı (Loxostege sticticalis L., 1761) Örneği
Authors: ÖZPINAR Ali
Published: August 2023

Abiyotik faktörlerden; sıcaklık ve nem yer yüzeyindeki canlıların yayılışı üzerinde önemli bir etkiye sahiptir. Sıcaklık ve nemdeki ani değişimler, bitkilerin gelişimini etkileyebileceği gibi, herbivor böceklerin popülasyon dalgalanmaları üzerinde de önemli değişiklikler yaratmaktadır. Dönemsel olarak Balkan ve Doğu Avrupa ülkelerinde salgın yapan Çayır tırtılı (Loxostege sticticalis L., 1761 Lepidoptera; Carambidae) 2022 yılı temmuz ayında Trakya bölgesinde yüksek popülasyon yoğunluğuna ulaşarak ayçiçeği alanlarında salgın yapmıştır. Salgın, Trakya bölgesinde mevsimin zamansız yağışlarıyla toprak neminin artması ve topraktaki pupalardan beklenin üzerinde ergin çıkışıyla ilişkilendirilmiştir. Yağış aynı zamanda yeni generasyonun larvaları için uygun vejetasyonun oluşmasını sağlamıştır. Bölgede bir önceki yıla göre ayçiçeği ekim alanlarındaki %5,6’lık artış da Çayır tırtılının konukçu bulmasını olumlu etkilemiştir. Benzer koşulların varlığı halinde, ülkemizde Çayır tırtılı önümüzde yıllarda da salgın yapma potansiyeline sahip olacaktır.
Sensitivity and identifiability analysis of a conceptual-lumped model in the headwaters of the Benue River Basin, Cameroon: implications for uncertainty quantification and parameter optimization
Authors: Nonki Rodric Mérimé , Amoussou Ernest , Lenouo André , Tshimanga Raphael Muamba , Houndenou Constant
Published: August 2023

Many hydrological applications employ conceptual-lumped models to support water resource management techniques. This study aims to evaluate the workability of applying a daily time-step conceptual-lumped model, HYdrological MODel (HYMOD), to the Headwaters Benue River Basin (HBRB) for future water resource management. This study combines both local and global sensitivity analysis (SA) approaches to focus on which model parameters most influence the model output. It also identifies how well the model parameters are defined in the model structure using six performance criteria to predict model uncertainty and improve model performance. The results showed that both SA approaches gave similar results in terms of sensitive parameters to the model output, which are also well-identified parameters in the model structure. The more precisely the model parameters are constrained in the small range, the smaller the model uncertainties, and therefore the better the model performance. The best simulation with regard to the measured streamflow lies within the narrow band of model uncertainty prediction for the behavioral parameter sets. This highlights that the simulated discharges agree with the observations satisfactorily, indicating the good performance of the hydrological model and the feasibility of using the HYMOD to estimate long time-series of river discharges in the study area.
Quantitative traceability study on the water quality driving forces across cities in the Xiang River Basin
Authors: Cao Yanmin , Qian Dongyue , Wang Chongyu
Published: August 2023

Based on the monthly monitoring data of water quality monitoring stations in the Xiang River Basin from 1990 to 2020, the Mann–Kendall test was used to analyze dissolved DO, BOD5, CODMn, TP, NH3-N,Cd, As, and Cr6+. The changing trend of nine indicators, including the stepwise regression method, was used to determine the cross city driving force of each water quality index, and the contribution rate (weight) of each driving force was obtained by principal component analysis. The research results show that (1) agriculture in Yongzhou is the main driving force, and its contribution rate is 67.2%; (2) urbanization has a greater impact on the driving process of the water environment in the Xiang River Basin, and its contribution rate is as follows: Changsha (83%) > Hengyang (80.7%) > Pingxiang (63.7%) > Chenzhou (60.9%) > Xiangtan (57.4%) > Zhuzhou (50%) > Loudi (48.5%); (3) the urbanization of Zhuzhou City and Loudi City's urban sewage discharge not only has an impact on the city's water environment, but also drives the water environment in the downstream Xiangtan area. The research results can provide a basis and reference for the study of water environmental governance in the basin.
Decolorization, COD and turbidity removal of the raw vinasse effluent by a one-step electro-oxidation process on a Pb/PbO2 anode
Authors: Alizadeh Rahimeh , Farhadi Khalil , Ghaneian Mohammad Taghi , Ehrampoush Mohammad Hassan , Jambarsang Sara , Salmani Mohammad Hossein , Motahhari Ali , Kokya Taher Ahmadzadeh
Published: August 2023

In this research, and for the first time, the application of anode Pb/PbO2 (prepared from combined thermal oxidation and electrochemical oxidation method) and steel cathode in a flow sample electrochemical treatment process of vinasse and in the wastewater of alcohol factories, has been investigated. The combination of electrodes of Pb/PbO2 as an anode, steel, and/or graphite as a cathode was used in the proposed electrochemical treatment setup. The efficiency of the proposed electrochemical treatment was determined by the removal percentage of chemical oxygen demand (COD), turbidity and color of vinasse samples. The response surface method (RSM) by Minitab 18 was used to determine the effect of the studied factors as well as to detect the relationship between variables. The results showed that under optimum conditions (Pb/PbO2 electrode as the anode and steel electrode as the cathode, a voltage of 30 V, pH 6.5, and reaction time of 45 min), the percentage reduction values of COD, turbidity and color were 97.7, 77.3 and 92.7%, respectively.
Sobre-excitabilidade (overexcitability) correlacionada à aptidão e talento musical
Authors: Koga Fabiana Oliveira , Rangni Rosemeire de Araújo
Published: August 2023

A presente pesquisa tem como objetivo estudar a produção da literatura (inter)nacional em relação à sobre-excitabilidade associada à aptidão e ao talento musical e caracterizá-la, descrevê-la e analisá-la tomando por base os últimos dez anos (2012 a 2022). Empreendeu-se a busca em seis (6) bases de dados, análise qualitativa e meta-análise (estatística descritiva e comparação de médias/T Student). Foram encontradas N=11 pesquisas (N=4 empíricas e randomizadas e N=7 teóricas), nenhuma delas era brasileira. Os Estados Unidos ficaram em primeiro no ranking das produções e suas fontes eram das áreas da Música, Artes em geral, Educação, Psicologia e Humanidades. As duas (2) pesquisas randomizadas mostraram diferenças significativas entre cantores e bailarinos (t (144)=1,636; p>0,05), bailarinos e atletas (t (109)=4,554; p<0,05) e músicos e não músicos (t (210)=1,170; p<0,05). Concluiu-se, que indivíduos talentosos em Música apresentam maior sobre-excitabilidade quando comparados com outros grupos.
Relationship Between Vitamin D Level and Index of Cardio
Electrophysiological Balance in Children
Authors: Guzelcicek Ahmet , Kilinc Emrullah , Fedai Halil , Dedeoglu Necip Fazıl , Toprak Kenan , Tascanov Mustafa Begenc , Ocak Metin , Gungorer Bülent , Kose Dogan
Published: August 2023

Background:

Vitamin D deficiency has been found to be associated with various cardiovascular
disorders, including hypertension, coronary artery disease, heart failure, peripheral vascular
diseases, and sudden cardiac death. In the literature, it has been reported that many electrocardiographic
parameters have been developed to predict ventricular arrhythmias. In recent studies,
it is noteworthy that the index of cardio-electrophysiological balance (iCEB) and correct cardioelectrophysiological
balance (iCEBc), which are electrocardiographic parameters, can be used as
new, easy, cheap and non-invasive parameters to predict ventricular arrhythmias.

Objective:

This study aimed to investigate the relationship between vitamin D deficiency and iCEB
and iCEBc values in children.

Methods:

A total of 186 patients were included in this study. Group 1 included 114 patients with vitamin
D levels below 20 ng/ml; 50 patients with vitamin D levels of 21-29 ng/ml were included in Group 2;
Group 3 consisted of 36 patients with a vitamin D level above 30 ng/ml. iCEB and iCEBc values were
calculated by taking 12-lead ECG from all individuals and comparing them between groups.

Results:

A total of 186 children, 114 subjects in Group 1, 36 subjects in Group 2, and 36 subjects in
Group 3, were included in the study. Demographic characteristics and height-weight values of the groups
were similar. Significant differences were found between the groups in terms of QT, QTc, QT/QRS, and
QTc/QRS levels (p: 0.003, 0.028, 0.001, and 0.001, respectively). In the correlation analysis, a negative
correlation was found between QTc/QRS and vitamin D level (r=-0.320, p=<0.001) and between
QT/QRS and vitamin D level (r=-0.268, p=<0.001). Moreover, vitamin D level (β=0.389, p<0.001) was
determined as an independent predictor of QTc/QRS in multivariate logistic regression analysis.

Conclusion:

iCEB and iCEBc parameters increase significantly in children with low vitamin D
levels. These parameters are also evaluated during the follow-up of children with vitamin D deficiency
in terms of the risk of ventricular arrhythmia. iCEBc can be used as an easy, inexpensive,
non-invasive, and reproducible parameter.
Identifying the Anti-inflammatory Effects of Astragalus Polysaccharides in
Anti-N-Methyl-D-Aspartate Receptor Encephalitis: Network Pharmacology
and Experimental Validation
Authors: Lu Yuling , Wu Ying , Sun Lanfeng , Yang Shengyu , Kuang Huimin , Li Rida , Meng Youshi , Wu Yuan
Published: August 2023

Background:

Astragalus polysaccharides (APS), a group of bioactive compounds obtained
from the natural source Astragalus membranaceus (AM), exhibits numerous pharmacological
actions in the central nervous system, such as anti-inflammatory, antioxidant, and immunomodulatory
properties. Despite the remarkable benefits, the effectiveness of APS in treating anti-
N-methyl-D-aspartate receptor (NMDAR) encephalitis and the corresponding mechanism have yet
to be fully understood. As such, this study aims to investigate the impact of APS on anti-NMDAR
encephalitis and explore the potential molecular network mechanism.

Methods:

The impact of APS intervention on mice with anti-NMDAR encephalitis was assessed,
and the possible molecular network mechanism was investigated utilizing network pharmacology
and bioinformatics techniques such as Gene Ontology (GO), Kyoto Encyclopedia of Genes and
Genomes (KEGG),protein–protein interaction (PPI) network, and molecular docking. Enzymelinked
immunosorbent assay (ELISA) was applied to detect the expression of core target proteins.

Results:

APS significantly ameliorated cognitive impairment and reduced susceptibility to PTZinduced
seizures in mice with anti-NMDAR encephalitis, confirming the beneficial effect of APS
on anti-NMDAR encephalitis. Seventeen intersecting genes were identified between APS and anti-
NMDAR encephalitis. GO and KEGG analyses revealed the characteristics of the intersecting gene
networks. STRING interaction in the PPI network was applied to find crucial molecules. The results
of molecular docking suggested that APS may regulate interleukin-1β (IL-1β), tumor necrosis
factor-α (TNF-α), and interleukin-6 (IL-6) as potential targets in anti-NMDAR encephalitis. Furthermore,
the levels of IL-1β, IL-6, and TNF-α detected by ELISA in anti-NMDAR encephalitis
mice were significantly downregulated in response to the administration of APS.

Conclusion:

The findings of this study demonstrate the significant role of APS in the treatment of
anti-NMDAR encephalitis, as it effectively suppresses inflammatory cytokines. These results suggest
that APS has the potential to be considered as a viable herbal medication for the treatment of
anti-NMDAR encephalitis.
Neuroprotective Effect of Lithospermum officinale Callus Extract on
Inflamed Primary Microglial Cells
Authors: Kheyrollah Maryam , Farhadpour Mohsen , Sabouni Farzaneh , Haghbeen Kamahldin
Published: August 2023

Background:

Plants that have therapeutic features for humans or animals are commonly
referred to as "medicinal plants". They produce secondary metabolites with antioxidant, antimicrobial
and/or anti-cancer effects. Lithospermum officinale, known as European stone seed, is a
famous medicinal herb. However, due to the pyrrolizidine alkaloids (PzAl) in the root extract of
L.officinal, there are therapeutic limitations to this herb.

Objective:

This research was devoted to the evaluation of the anti-inflammatory capacity of methanolic
extracts of L. officinale callus (LoE) (fresh cells) on rat microglial cells, the immune cells of
the Central Nervous System, which play an essential role in the responses to neuroinflammation.

Methods:

Primary microglia were obtained from neonatal Wistar rats (1 to 3-days old), and then
treated with various concentration of CfA and methanolic extracts of 17 and 31-day-old L. officinale
callus before LPS-stimulation. In addition to HPLC analysis of the extracts, viability, nitric
oxide production, and evaluation of pro-inflammatory genes and cytokines in the inflamed microglia
were investigated by MTT, Griess methos, qrt-PCR, and ELISA.

Results:

Methanolic extract of the 17-day-old callus of L. officinale exhibited anti-inflammatory
effects on LPS-stimulated microglial cells much higher than observed for CfA. The data were
further supported by the decreased expression of Nos2, Tnf-α, and Cox-2 mRNA and the suppression
of TNF-α and IL-1β release in the activated microglial cells pretreated with the effective dose
of LoE (0.8 mg mL-1).

Conclusion:

It was assumed that the better anti-neuroinflammatory performance of LoE than CfA
in LPS-activated primary microglia could be a result of the synergism of the components of the
extract and the lipophilic nature of RsA as the main phenolic acid of LoE. Considering that LoE
shows a high antioxidant capacity and lacks PzAl, it is anticipated that LoE extract might be considered
a reliable substitute to play a key role in the preparation of neuroprotective pharmaceutical
formulas, which require in vivo research and further experiments.
Sex-Specific Variations in Vitamin D and Vitamin D Binding Protein
(Vdbp) and Flipped Pattern of their Association in Preeclamptic Women
with Dyslipidemia
Authors: Simmi Kharb , Chetna Bhatotiya , Smiti Nanda , Gurpreet Gill
Published: August 2023

Background:

Women with preeclampsia frequently have disorders of calcium metabolism during their pregnancies, including hypocalciuria and low vitamin D levels. The link between fetal sex and vitamin D and vitamin D binding protein levels in pregnancy and preeclampsia is not clear.

Aim:

Present study was designed to explore sex-specific variations in vitamin D and vitamin D bind-ing protein levels in maternal and umbilical cord blood samples for age and gestation matched 20 primigravida normotensive and 20 primigravida with preeclampsia with a singleton pregnancy.

Methods:

Routine investigations, vitamin D binding protein (VDBP), and serum vitamin D in both maternal serum and cord blood were measured in both groups. Association between maternal and cord blood vitamin D levels was highly positive in both cases and control with babies of either sex.

Results:

This association exhibited sexual dimorphism between cord blood vitamin D and vitamin D binding protein (VDBP) suggesting that the occurrence of pregnancy complications differs according to fetal sex with a higher cardiovascular and metabolic load for the mother in the presence of a male fetus.

Conclusion:

Vitamin D binding protein (VDBP) measurement along with vitamin D level should be advised in clinical practice for early detection and screening.
Spousal Postpartum Social Support and Association with Sexual Function and Sexual Quality of Life among Breastfeeding Women
Authors: Bahrami Nasim , Barghamadi Sanaz , Asgari Somayeh , Lissack Kerrie , Griffiths Mark D. , Alimoardi Zainab
Published: August 2023

Background:

The present study investigated the relationship between spousal support during postpartum period and the sexual function and sexual quality of life among lactating women.

Methods:

A cross-sectional study was carried out from October to March 2021 comprising 301 breastfeeding women referred to comprehensive health centers in Qazvin, Iran. The sampling method was
performed in two stages. First, via cluster sampling, ten comprehensive health centers were randomly
selected. Then based on the list of lactating mothers, 31 mothers in each center were randomly selected and invited to complete the survey. The survey included three psychometric instruments (i.e., Postpartum Partner Support Scale [PPSS], Female Sexual Function Index [FSFI] and Sexual Quality of
Life-Female version [SQOL-F]) as well as a reproductive and demographic information questionnaire.
The findings were analyzed using univariable and multivariable regression models at a significance
level of p< 0.05.

Results:

The participants had a mean age of 30.33 years and a mean length of marriage of 7.99 years.
A total of 219 women were exclusively breastfeeding (72.8%), and 82 used a combination of breastfeeding and formula milk to feed their infants (27.2%). The mean (standard deviation) scores in the
postpartum period were 61.34 out of 80 (SD=11.41) on the PPSS, 86.52 out of 108 (SD=19.68) on the
SQOL-F, and 22.78 out of 36 (SD=6.20) on the FSFI. Based on adjusted multivariable regression
models, spousal support in the postpartum period was a significant predictor of sexual quality of life
(β=0.39, p< 0.001) but not a significant predictor of sexual function. Sexual quality of life was a significant predictor of sexual function (β=0.44, p< 0.001).

Conclusion:

Given the importance of spousal support during the postpartum period in relation to sexual quality of life and sexual function, designing and implementing counseling programs based on the
role of spousal support during postpartum sexual life would be helpful.
An Automatic Method Framework for Personalized Knee Prosthetic Modeling
Based on Kinematic Geometry
Authors: Li Pengxi , Liu Hui , Zhang Bocheng , Liu Dongpei , Yang Liang , Liu Bin
Published: August 2023

The shape of a knee prosthesis has an important impact on the effect of total knee arthroplasty. Comparing to a standard common prosthesis, the personalized prosthesis has inherent advantages. However, how to construct a personalized knee prosthesis has not been studied deeply. In this paper, we present an automatic method framework of modeling personalized knee prostheses based on shape statistics and kinematic geometry. Firstly, the average healthy knee model is established through an unsupervised process. Secondly, the sTEA (Surgical Transecpicondylar Axis) is calculated, and the average healthy knee model is resized according to it. Thirdly, the resized model is used to simulate the knee's motion in a healthy state. Fourthly, according to the target patient's condition, an excising operation is simulated on both patient's knee model and the resized model to generate an initial knee prosthesis model. Finally, the initial prosthesis model is adjusted according to the simulated motion results. The average maximum error between the resized healthy knee model and the patient's own knee model is less than 2 mm, and the average maximum error between the motion simulation results and actual motion results is less than 3 mm. This framework can generate personalized knee prosthesis models according to the patient's different conditions, which makes up for the deficiencies of standard common prostheses.
Regulatory Effects of the Silymarin on Expression of OCT4, NANOG, and
P53 in MCF7 Cell Lines
Authors: Hajizadeh Mohammad Reza , Ranjbar Faezeh Esmaeili , Abasi Ali , Abbasifard Mitra , Mahmoodi Mehdi , Noroozi-Karimabad Mojgan
Published: August 2023

Background::

Breast cancer was known as the second most common cause of death in the world, natural sources compound derived from milk thistle called silymarin had already shown anticancer properties.

Objective:

In the present study, silymarin was used to treat MCF7 cells and inhibition of stem cell pluripotency genes, as well as cell proliferation.

Methods:

MCF7 cells were cultured in the presence of RPMI-1640 medium consisting of various silymarin extract concentrations (10, 100, 500, 1000, 2000, 3000, 4000, and 5000 µg/mL) for 24, 48, and 72 hours. The inhibitory effects of the compound on cellular proliferation were assessed via employing MTT assay techniques. Following confirming apoptosis, the fold changes of OCT4, NANOG and P53 expression were determined by quantitative Real-Time PCR.

Results:

There was a significant difference (p value< 0.05) in cell viability when various concentrations of silymarin extract were used for 24, 48, and 72 h in comparison to the control. Real-Time- PCR analysis indicated that the expression of OCT4 and NANOG was downregulated while P53 upregulated in compare to untreated control cells (p value < 0.05).

Conclusion:

According to these findings, the silymarin effects on MCF7 cell line and act via modulating OCT4, NANOG, and P53 pathway mediators. Silymarin may introduce this compound as a promising therapeutic compound against MCF7.
Detection of Brain Tumor Employing Residual Network-based Optimized Deep Learning
Authors: Rohilla Saransh , Jain Shruti
Published: August 2023

Background:

Diagnosis and treatment planning play a very vital role in improving the
survival of oncological patients. However, there is high variability in the shape, size, and structure
of the tumor, making automatic segmentation difficult. The automatic and accurate detection and
segmentation methods for Brain tumors are proposed in this paper.

Methods:

A modified ResNet50 model was used for tumor detection, and a ResUNetmodel-based
convolutional neural network for segmentation is proposed in this paper. The detection and segmentation
were performed on the same dataset consisting of pre-contrast, FLAIR, and postcontrast
MRI images of 110 patients collected from the cancer imaging archive. Due to the use of
residual networks, the authors observed improvement in evaluation parameters, such as accuracy
for tumor detection and dice similarity coefficient for tumor segmentation.

Results:

The accuracy of tumor detection and dice similarity coefficient achieved by the segmentation
model were 96.77% and 0.893, respectively, for the TCIA dataset. The results were compared
based on manual segmentation and existing segmentation techniques. The tumor mask was
also individually compared to the ground truth using the SSIM value. The proposed detection and
segmentation models were validated on BraTS2015 and BraTS2017 datasets, and the results were
consensus.

Conclusion:

The use of residual networks in both the detection and the segmentation model resulted
in improved accuracy and DSC score. DSC score was increased by 5.9% compared to the
UNet model, and the accuracy of the model was increased from 92% to 96.77% for the test set.
Extraction and Analysis of Lipid Raft Proteins with Detergent-and Non
detergent-based Method
Authors: Chen Yuchu , Liu Hongbei , Michael Adu-Frimpong , Gu Chenlu , Zhao Lu , Tian Sheng , Li Xiu , Cao Xia , Tong Shanshan
Published: August 2023

Introduction:

Lipid raft is found on the cell membrane and is considered a microstructure
rich in cholesterol, phospholipids and target proteins that are insoluble in nonionic detergents at low
temperatures.

Methods:

In this study, detergent and non-detergent methods were used to extract lipid rafts from different
cells. With β-cyclodextrin as the negative control group, we analyzed and compared the effects
of different extraction methods on the composition of lipid rafts in Caco-2 and U251 cells using three
kinds of lysate, namely detergent method 1, detergent method 2 and non-detergent method, which
could be extracted and collected via sucrose density gradient centrifugation. Western blotting and
immunofluorescence were utilized to determine the location of lipid rafts via the proteins Caveolin-1
and Flotillin-1, which are the characteristic proteins P-gp and TrkA in cells. The total protein in the
lipid raft was quantitatively determined through the BCA (detergent compatible) kit method.

Results:

The results showed that the total amount of lipid raft proteins extracted via the detergent
method was more than that of the non-detergent method, while the content of β-cyclodextrin control
histone that caused disruption of lipid rafts structure was the lowest.

Conclusion:

The detergent method extracted more abundant lipid rafts than the non-detergent method.
Detergent method 2 did not only extract more fat raft layers, but also the extracted highest total protein
content, wherein it demonstrated better extraction effect with more lipid raft layers and higher expression
of target protein P-gp.
Drug-target Binding Affinity Prediction Based on Three-branched
Multiscale Convolutional Neural Networks
Authors: Lu Yaoyao , Liu Junkai , Jiang Tengsheng , Cui Zhiming , Wu Hongjie
Published: August 2023

Background:

New drugs are costly, time-consuming, and often accompanied by safety concerns. With the development of deep learning, computer-aided drug design has become more mainstream, and convolutional neural networks and graph neural networks have been widely used for drug–target affinity (DTA) prediction.

Objective:

The paper proposes a method of predicting DTA using graph convolutional networks and multiscale convolutional neural networks.

Methods:

We construct drug molecules into graph representation vectors and learn feature expressions through graph attention networks and graph convolutional networks. A three-branch convolutional neural network learns the local and global features of protein sequences, and the two feature representations are merged into a regression module to predict the DTA.

Results:

We present a novel model to predict DTA, with a 2.5% improvement in the consistency index and a 21% accuracy improvement in terms of the mean squared error on the Davis dataset compared to DeepDTA. Morever, our method outperformed other mainstream DTA prediction models namely, GANsDTA, WideDTA, GraphDTA and DeepAffinity.

Conclusion:

The results showed that the use of multiscale convolutional neural networks was better than a single-branched convolutional neural network at capturing protein signatures and the use of graphs to express drug molecules yielded better results.
A Short Appraisal of Magnetic Nanoparticles for Breast Cancer: In vitro
and In vivo Research
Authors: Kaur Simranjeet , Singh Dilpreet
Published: August 2023

The increasing incidence of breast cancer and the associated morbidity due to higher metastasis
created the urge to develop a nanocarrier that can be used as a potent therapeutic carrier with targeting
efficacy. The use of superparamagnetic nanoparticles in breast cancer research and treatment has
gained considerable attention in recent years. Magnetic nanoparticles (MNPs) can be used to construct
nanocarriers since they possess superior properties such as superparamagnetism, easy surface functionalization
to attach ligands, and non-immunogenic. MNPs are superior carriers that are used to target
cancer cells without harming the normal cells in the body, which leads to therapeutic efficacy in the
body. Along with their established anticancer potential and enhanced drug concentration at endosomal
pH, the superparamagnetic property of MNPs was further exploited for their applications in reticuloendothelial
uptake, drug delivery, medical imaging, and theranostics applications in breast cancer. Moreover,
the clinical translational of MNPs, along with future prospects and key challenges in vivo, have
been duly presented in the final review. The scientists preferred the ongoing research in MNPs due to
their high biocompatibility and ease of targeting at molecular and cellular levels. The review highlighted
the in vitro and in vivo research and patent supported data for potential use of MNPs for the treatment
of breast cancer.
Neuropsychiatric Symptoms are Related to Blood-biomarkers in Major
Neurocognitive Disorders
Authors: Marques-Aleixo Inês , Sampaio Arnaldina , Bohn Lucimére , Machado Flavia , Barros Duarte , Ribeiro Oscár , Carvalho Joana , Magalhães José
Published: August 2023

Background:

Neuropsychiatric symptoms (NPS) are highly prevalent among individuals
with major neurocognitive disorders (MNCD). Objective: Here, we characterized blood biomarkers
(metabolic, inflammatory, neurotrophic profiles and total antioxidant), body composition, physical
fitness and quality of life (QoL) in individuals with MNCD according to NPS.

Methods:

The sample comprised 34 older adults (71.4% women; 74.06±6.03 yrs, with MNCD diagnosis) categorized according to 50th percentile [Low (≤12) or High (≥13)] for NPS (Neuropsychiatric Inventory Questionnaire). Sociodemographic, clinical data, body composition, anthropometric, cognitive assessment (ADAS-Cog), physical fitness (Senior Fitness Test), QoL (QoLAlzheimer’s Disease scale) were evaluated, and blood samples were collected for biochemical
analysis.

Results:

Low compared to high NPS group showed higher levels of IL-6, IGF-1and neurotrophic zscore (composite of IGF-1, VEGF-1, BDNF). Additionally, low compared to high NPS group have
higher QoL, aerobic fitness and upper body and lower body strength.

Conclusion:

The severity of NPS seems to be related to modified neurotrophic and inflammatory
outcomes, lower physical fitness, and poor QoL. Strategies to counteract NPS development may
preserve the physical and mental health of individuals with MNCD.
An Overview of Deep Learning-Based Recommendation Systems and Evaluation Metrics
Authors: Venkatesiah Sheela Samudrala , Rathnaiah Radhika Kotrike
Published: August 2023

The ever-increasing information on the internet and the rapid development
of online movies, songs, and stores have enhanced the demands of customers to obtain
the required information within the least time. A Recommendation System (RS) is
designed to help customers with personalized information and interests to avoid
overloading issues in entertainment and social media. Though traditional methods have
made noteworthy developments, RS encounters challenges such as data limitations and
cold starts. The present study aims to review the developments in the field of deep
learning-based RS, thereby providing the required information for researchers. In
addition, several applicable domains of employing deep learning-based RS have been
analyzed. The review has been organized into RS type, deep learning approaches, deep
learning-based recommendation systems in various applications, and evaluation
metrics.
Towards Recommender Systems Integrating Contextual Information from Multiple Domains through Tensor Factorization
Authors: Véras Douglas , Nascimento André , Callou Gustavo
Published: August 2023

Traditionally, single-domain recommender systems (SDRS) can suggest
suitable products for users to alleviate information overload. Nonetheless, cross-domain recommender systems (CDRS) have enhanced SDRS by accomplishing
specific objectives, such as improving precision and diversity and solving cold-start
and sparsity issues. Rather than considering each domain separately, CDRS uses
information gathered from a particular domain (e.g., music) to enhance
recommendations for another domain (e.g., films). Context-aware Recommender
System (CARS) focuses on optimizing the quality of suggestions, which are more
appropriate for users depending on their context. Integrating these techniques is helpful
for many cases where knowledge from several sources can be used to enhance
recommendations and where relevant contextual information is considered. This work
describes the main challenges and solutions of the state-of-the-art in Cross-Domain
Context-Aware Recommender Systems (CD-CARS), taking into account the
abundance of data on different domains and the systematic adoption of contextual data.
CD-CARS have shown efficient methods to tackle realistic recommendation scenarios,
preserving the benefits of CDRS (regarding cold-start and sparsity issues) and CARS
(assuming accuracy). Therefore, CD-CARS may direct future research to recommender
systems that use contextual information from multiple domains in a systematic way.
A Personalized Artificial Neural Network for Rice Crop Yield Prediction
Authors: Shaker Reddy Pundru Chandra , Sureshbabu Alladi , Sucharitha Yadala , Narayana Goddumarri Surya
Published: August 2023

Early and accurate crop yield estimates at a local and national level are
essential to oversee industry and trade planning and to mitigate the price hypotheses.
The major challenge for farmers in the agricultural field is selecting an appropriate crop
for planting. Crop selection is dependent on several factors like climate, soil nature,
market, etc. Majorly, crop yield production depends on weather conditions and soil
types. Yield anticipation is essential for farmers nowadays, which significantly adds to
the appropriate yield selection for sowing. There needs to be a framework to
recommend what type of crops to produce for farmers. It is essential and challenging to
make the right farming decisions at a future steady cost and yield balance. This article
proposes an Artificial Neural Network (ANN) model for rice crop yield prediction by
utilizing weather parameters like rainfall, temperature, sunshine hours, and
evapotranspiration. Generally, Default-ANN has only one hidden layer. But in this
work, a Personalized Artificial Neural Network (PANN) approach has been designed
by varying the number of hidden layers, the number of neurons, and the learning rate.
P-ANN model accuracy is computed using R-Square (R2) and Percentage Forecast
Error (PFE). Outcomes demonstrate that the P-ANN model performs precisely with a
greater R2 and smaller PFE values than existing methods. For this research, the
seasonal (Kharif & Rabi) weather dataset and rice yield data of Guntur district, Andhra
Pradesh, India, from 1997-2014 have been used. Better paddy yield was forecasted by
utilizing the P-ANN approach.