bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reliability and Validity Assessment of the Turkish Version of the Emotional Availability and Responsiveness in Intrapartum Care Scale (EAR-IC)
Authors: Varişoğlu Y Yildirim , Vural P Irmak 
Published: September 2024

Background:
Emotional availability and responsiveness in intrapartum care increase women’s birth satisfaction, comfort, and quality of nursing/midwifery care. In Turkey, there is no instrument for measuring emotional availability and responsiveness in intrapartum care.

Aim:
An established translation, reliability, and validation approach was used to obtain the Turkish form of the A-EAR-IC scale.

Methods:
A descriptive cross-sectional study was used. This study was conducted in Istanbul, Turkey. The study sample consisted of 132 Turkish midwives working in the birth room. The construct validity of the scale was tested using confirmatory factor analysis, whereas the concurrent scale validity was tested using the Emotional Labor Behavior Scale. Internal consistency analysis was performed to test the reliability of the scale.

Results:
The results of the EAR-IC measure showed that it fit the one-factor model. The goodness-of-fit indices of the one-factor model containing eight items were acceptable. The instrument showed satisfactory content validity (I-CVI =0.80–1.00, S-CVI =0.88). According to CFA, the structure with one factor showed acceptable model fit (χ2/df: 1.74, CFI: 0.96, IFI: 0.96, RMSEA: 0.07, and SRMR: 0.02). Concurrent validity of the instrument was assessed with the “in-depth behavior” subscale of the Emotional Labor Behavior Scale for Nursing. Spearman’s correlations revealed that the EAR-IC was moderately positively correlated with the in-depth behavior concern subscale of the ELBS (rho =0.62, P < 0.001).

Conclusion:
The Turkish version of the EAR-IC is a suitable, effective, and reliable instrument for measuring the emotional aspects of intrapartum caregiving midwives.
Comparing Skin Tones to Shades of Upper Anterior Teeth: An Observational Study among Nigerian Adults
Authors: Ikusika OF , Idon PI , Adenuga-Taiwo OA , Umanah AU , Akinboboye BO , Akeredolu PA 
Published: September 2024

Background:
Skin tones may be predictive of anterior tooth shade. An analysis of the relationship of skin tone to anterior tooth shade within a population may facilitate the prescription of complete dentures with optimal esthetics and realism.

Aim:
This study aimed to assess the relationship between skin tones and shades of upper anterior teeth among a diverse Nigerian adult population.

Methods:
The study was conducted between February and December 2022 among consecutive healthy adults recruited from dental clinics of five teaching hospitals in four cities located across four geopolitical zones of Nigeria. Skin tones were recorded using the National Immigration Service (NIS) Skin Color scale. The Vitapan Classic® shade guide was used to record the tooth shade of the upper central incisors and canines. Bivariate and multivariate tests of associations were performed, and predictive probabilities were developed.

Results:
A total of 1070 adults with a mean age of 30.8 ± 10 years participated in the study. NIS skin tones 6 (29.8%) and 5 (27.7%) were the most common, while hues A (48.7% in incisors and 48.4 % in canines) and B (41.1% in incisors and 40.1% in canines) were predominant. The values of the incisors and canines were positively related to skin tone (P = 0.001) and age (P = 0.001 for incisors and P = 0.003 for canines). Hue was also significantly associated with skin tone for canines (P = 0.01) and gender (P = 0.001). Regression models showed increasingly darker skin tones were positively related to duller values for incisor teeth (β = 0.173, OR =1.19, P < 0.001). The values for canines were more likely to be >2 (β = 0.118, OR =1.12, P = 0.01), but without significant positive relationship to increasingly darker skin tones (β = -0.99, OR =0.91, P = 0.22).

Conclusion:
Skin tones were associated with upper anterior tooth shade. There was a positive relationship between skin tones and the shades of upper anterior teeth. Skin tones could guide the clinician in shade selection during complete denture prescriptions.
Modeling Fragile X Syndrome: Characterizing Fmr1 Gene Knockout Mice across Genotype, Behavior, and Morphology
Authors: Liao Yonghua , Liu Junjie , Zhang Jinyuan , Chen Da , Liu Yifan , Li Zhuolin , Su Hao , Tang Jiaye , Chen Shengqiang 
Published: September 2024

ABSTRACT

Objective:
Fragile X syndrome (FXS) is a hereditary condition resulting from dynamic mutations in the Fmr1 gene, leading to reduced or absent fragile X mental retardation protein (FMRP). Although molecular genetic diagnostics for FXS have advanced, there is currently a lack of effective clinical treatment. Hence, identifying a suitable FXS animal model is crucial for FXS therapeutic studies. This study primarily aims to evaluate whether Fmr1 mice display aberrant behaviors similar to those observed in human FXS patients, aiming to validate their feasibility as a model for FXS.

Research Design and Methods:
Polymerase chain reaction and immunoblotting were employed to genotype Fmr1 knockout (KO) mice and wild-type (WT) mice. Using hematoxylin and eosin staining and Golgi staining, testicular and dendritic spine morphology in both animal groups was observed. The animals were subjected to audiogenic seizure (AGS) observation, evaluating their learning and memory levels using the water maze and light avoidance methods, while their exploratory behavior capacity was assessed using the open-field and autonomous activity experiments. Anxiety behavior was evaluated using the elevated plus maze.

Results:
WT mice produced a 500 bp DNA fragment, while KO mice yielded an 800 bp fragment. KO mice exhibited a lack of FMRP expression, notably increased dendritic spine length and density, and presented with macroorchidism. KO mice showed increased susceptibility to AGS. In the Morris water maze experiment, KO mice displayed significantly increased escape latency and platform crossings. The spatial search experiment revealed that KO mice spent significantly less time in the target quadrant compared to WT mice. The light-avoidance and platform experiments indicated a significant reduction in latency for KO mice but an increase in errors. In the open-field experiment, KO mice spent significantly more time and made more entries into the central zone compared to WT mice. KO mice exhibited increased total travel distance and speed. The elevated plus maze experiment showed that KO mice spent significantly more time, made more entries, and traveled further in the open area compared to WT mice. The autonomous activity experiment revealed that KO mice had significantly more activity counts than WT mice but fewer standing counts.

Conclusions:
Findings from genotype, protein expression spectrum, morphology, and behavioral results suggest that Fmr1 gene KO mice demonstrate phenotypes similar to those observed in human FXS, making them a potential model for future FXS research. These phenotypes are associated with the absence of FMRP expression.
Blockade of Src signaling prevented stemness gene expression and proliferation of patient-derived gastric cancer stem cells
Authors: Hu Chi-Tan , Lin Chen-Fang , Shih Hsiu-Ming , You Ren-In , Wu Wen-Sheng , Chen Yen-Cheng 
Published: September 2024

ABSTRACT

Objectives:
Gastric cancer (GC) is one of the most malignant tumors. Mounting studies highlighted gastric cancer stem cells (GCSCs) were responsible for the failure of treatment due to recurrence and drug resistance of advanced GC. However, targeted therapy against GCSC for improving GC prognosis suffered from lack of suitable models and molecular targets in terms of personalized medicine. To address this issue, two patient-derived GC cell lines SD209 and SD292 with cancer stem cells (CSCs) such as phenotype were isolated for establishing targeted therapy aiming at critical metastatic signaling in GC.

Materials and Methods:
The primary patient-derived GCSCs were established from parts of GC tissues for characterization of stem cells (SCs) phenotype at both cellular and molecular levels. Western blot and Immunohistochemistry (IHC) were performed for identifying the deregulated signaling in GC tissue. Immunofluorescence was used for analyzing proliferating and SC markers in GCSC attached on fibroblast. Acridine orange and propidium iodide analyses were performed for the survival of GCSC in suspensions.

Results:
In the culture environments of both SD209 and SD292, a lot of mesenchymal fibroblasts spread and crowd together on which a lot of cell clumps, suspected as GCSC, were firmly attached. In the IHC analysis, the GCSC stemness genes CD44 and Ep-CAM increased in tumor tissues of SD209, whereas Nanog-1 and octamer-binding transcription factor 3 (OCT-3) increased in that of SD292. By immunofluorescent analysis of a proliferation marker Ki67, the growth of SD209 and SD292 on mesenchymal fibroblasts was found to be reduced by dasatinib, the inhibitor of the Src kinase whose activity was upregulated in tumor tissues of both GCs. Dasatinib also suppressed the expression of Nanog-1 and OCT-3 in SD292 attached on mesenchymal fibroblasts.

Conclusion:
This study may provide a base for targeted therapy against GCSCs/GCs progression in future preclinical/clinical settings.
Is intranasal dexamethasone effective in reducing sore throat following surgery?
Authors: Shetabi Hamidreza , Momeni Ehsan 
Published: September 2024

ABSTRACT

Objectives:
Postextubation, individuals may experience the discomfort of a sore throat. Our main aim of the study was to investigate if intranasal dexamethasone is successful in reducing postoperative sore throat occurrence.

Materials and Methods:
The study involved 96 adult individuals who were scheduled for elective eye surgery at Faiz Medical Center, which is affiliated with Isfahan University, between July 2020 and March 2021. The individuals were assigned by chance to two cohorts of 48 people each, with one cohort getting dexamethasone (IND) through the nose and the other cohort getting normal saline (INS) through the nose right after the endotracheal tube insertion. The presence of symptoms such as aching throat, cough, and hoarseness after surgery was recorded and examined with version 23 of the SPSS software.

Results:
Upon analysis, it was observed that there were no statistically significant alterations in demographic attributes, tracheal intubation variables, duration of surgery, and postoperative outcomes (P < 0.05). Within the IND cohort, a notable 80.2% decrease in the occurrence of sore throat was noted immediately following the surgical procedure, along with a 34% reduction within the initial 6 hours of hospital stay (P < 0.001). Moreover, dexamethasone also decreased the occurrence of cough and hoarseness by 31.7% and 38.2% during recovery, as well as 19% and 25.4% within the initial 2 h upon admission to the ward (P < 0.001).

Conclusion:
The current study showcased the preventive impact of dexamethasone intranasally in decreasing the occurrence of sore throat in the early stages of postoperative period. Nevertheless, its efficacy diminished after 6 h. Furthermore, the intranasal application of dexamethasone exhibited the ability to alleviate hoarseness and cough within the first 2 h following surgical intervention.
Performance of Artificial Intelligence Models (Bidirectional Encoder Representations from Transformers + TextCNN) in Detecting Eight Psychiatric Diagnoses from Unstructured Texts Chinese Electronic Medical Records
Authors: Lo Yi-Fan , Tai Yueh-Ming 
Published: September 2024

Objectives:
Advances in artificial intelligence (AI) have revolutionized various industries, including health care. In this study, we intended to explore the capability of AI assistants in psychiatric diagnoses. To achieve this goal, we proposed a series of deep active learning models, namely bidirectional encoder representations from transformers (BERT) – TextCNN. These models combine the strengths of two powerful techniques: BERT and convolutional neural network (CNN) for the text.

Methods:
We collected 21,003 Chinese psychiatry electronic medical records (EMRs) and developed two types of models: a multi-diagnosis classifier and eight single-diagnosis classifiers for schizophrenia (SCZ), major depressive disorder (MDD), manic state (MANIA), adjustment disorder (ADJ), substance use disorder (SUD), personality disorder (PD), attention-deficit/hyperactivity disorder (ADHD), and autistic spectrum disorder (ASD). Their performance was compared through plotting receiver operating characteristic curves and assessing the performance, area under curve (AUC) using the DeLong test.

Results:
This study showed the excellent performance of our BERT + TextCNN models in detecting almost all eight psychiatric diagnoses, achieving AUCs being greater than 0.9, except for the single-diagnosis classifier for ADHD (AUC = 0.83).

Conclusion:
This study highlights the promising applicability of the BERT + TextCNN model as a diagnostic assistant for psychiatry diagnoses derived from EMRs. Being consistent with previous findings, the single-diagnosis classifiers generally outperform the multi-diagnosis classifier in predicting most diagnoses, though not all. Further studies are warranted to confirm whether the specific characteristics of illnesses contribute to the performance gap between multi- and single-diagnosis classifiers.
Influences of Potential Military Conflict between Taiwan and China on the Intention to Emigrate among Taiwanese Individuals
Authors: Lin Yu-Hsuan , Yen Cheng-Fang 
Published: September 2024

Objectives:
Amid escalating tensions between Taiwan and China, the potential for military conflict profoundly affects the intention to emigrate among Taiwanese citizens. This study examined the influence of the perception of potential military conflict between Taiwan and China, depressive symptoms, and sociodemographic characteristics on emigration intention among Taiwanese citizens.

Methods:
Through stratified random sampling and telephone interviews conducted, data on emigration intentions, awareness of China’s military activities, concern of potential military conflict, the perceived effects of military conflict on the lifestyle, and depressive symptoms of 2,000 Taiwanese individuals were collected. Logistic regression models were used to analyze the associations between emigration intention and various predictors, namely, attitudes toward military conflict, depressive symptoms, and sociodemographic characteristics.

Results:
The findings revealed that 23.4% of participants harbored the intention to emigrate. Significant determinants of this intention were high education level (p < 0.001), concern about the possibility of military conflict (p < 0.001), concern about the effects of military conflict (p < 0.001), a marked lack of confidence in the ability of political leadership to manage cross-strait relations (p < 0.001), and significant depressive symptoms (p < 0.001). Conversely, older age (p < 0.001), lack of awareness about China’s military activities (p < 0.001), and chronic physical ailments (p < 0.05) were inversely related to emigration intention. Age and education level moderated the relationship between military conflict-related concern and emigration intention (p < 0.05).

Conclusion:
The interplay of age, educational level, concern about military conflict between Taiwan and China, significant depressive symptoms, and confidence in political leadership significantly influences emigration intention among Taiwanese individuals, highlighting the need for targeted policy responses.
Anomaly detection and prediction of energy consumption for smart homes using machine learning
Authors: Ambat Anitha , Sahoo Jayakrushna 
Published: September 2024

AbstractAs technology advances, smart homes are being increasingly adopted, thus generating massive data that pose new research challenges. We propose a machine learning framework for monitoring energy consumption in smart home devices. The proposed framework involves an anomaly detection module, followed by a predictive model to forecast energy consumption patterns in a typical smart home. We employ three outlier‐based techniques for anomaly detection: (1) local outlier factor, (2) connectivity‐based outlier factor, and (3) cluster‐based local outlier factor. Furthermore, we apply random forest, linear regression, decision tree, and the ensemble techniques of adaptive, gradient, and extreme gradient boosting to anomaly free data to develop baseline models that predict the energy consumption patterns of smart home devices. The framework is evaluated on three publicly available energy datasets collected from various smart homes. The experimental results reveal that the cluster‐based local outlier factor with extreme gradient boosting achieves promising results with high prediction accuracy.
A compact dual‐band bandpass filter based on coupled stub‐loaded square ring resonators by using transversal signal‐interaction concepts
Authors: Xu Dong‐sheng , Wang Li‐tian , Qian Li‐rong , Li Cui‐ping , Tian Ya‐hui , Li Hong‐lang , Chen Xuan , Li Yu‐qi 
Published: September 2024

AbstractIn this paper, a novel dual‐band wideband bandpass filter (BPF) based on transversal signal‐interaction concepts with a wide upper stopband is proposed and investigated. The designed specification of two passbands can be managed and satisfied based on the independent controllable fractional bandwidth of the two passbands and the centered frequencies. The centered frequencies of dual‐band BPF are, respectively, 0.79 GHz (ƒ1) and 1.24 GHz (ƒ2) with 3 dB fraction bandwidths of 26.54% and 11.3%. Two transmission paths consisting of coupled stub‐loaded square ring resonators and anti‐coupled shorted lines are used to realize signal cancellation of multiple transmission path signal transmission from Port 1 to Port 2. Eleven transmission zeros (TZs) modify harmonic suppression up to 10 ƒ1 with stopband rejection higher than 15 dB. Butterworth lumped notch network and step impedance resonator (SIR) are also utilized to improve the selectivity and harmonic suppression. A compact filter with a circuit size of 0.08λg × 0.08λg is implemented and tested. Good agreement between simulation and measured results verifies the reliability of the designing scheme.
The Role of the Writing Centre in Enhancing Student Employability a Case Study of a University in South Africa
Authors: Ntando Yamkela , Ndamase Maxhobandile , Matenda Stenford 
Published: September 2024

In the quest of bridging the high graduate unemployment gap of South African graduates, universities introduced initiatives which sought to assist graduates gain the necessary working experience. South African graduates struggle to find sufficient employment due to lack of work experience. The peer-to-peer coaching led by senior students, who are postgraduate students, leads to the enhancement of graduate employability. Therefore, this paper sought to investigate the contribution of the writing centre in increasing the chances of employment for its consultants. In light of this, literature was consulted to reveal the role of the writing centre in promoting student employment. Unstructured interviews were conducted on fifteen (15) participants, who were identified through convenient sampling technique. Thematic analysis was adopted to analyse the collected data. The paper found that the programme equips students to be familiar with the working environment. The study further revealed that the writing centre office is under-capacitated financially. The study thus proposed that the programme must be financially supported by the university, Department of Higher Education and Training (DHET) and other external stakeholders to continue capacitating the development of students. The study, therefore, concluded that the writing centre office effectively promotes student employability and increases the working experience of writing centre consultants.
Neuro-Logocentric Representation of Brain Activity as a Conceptual Basis for Artificial Neural Networks: a Critical Analysis
Authors: Zhelnin Anton I. 
Published: September 2024

The aim of this article is a critical analysis of the logocentric representation of brain as a conceptual basis for artificial neural networks (ANNs). Neuro-logocentrism turns possible mostly because of some similarities of brain activity to logical reasoning. They are discrete mode of action, semblance of realization of logical connectives and inferences, presence of two “values” (“all-or-none” principle). It is demonstrated that they are only loose analogies. The idea that neurons are net nodes that realize logical and computational operations as quite simple automata is based on the concept of connectionism that appears a new special type of hylemorphism. On the contrary, brain activity demonstrates the primacy of the whole and its irreducibility to the aggregative sum of elements and parts. It appears continual totality, where everything is reciprocally interconnected, so that it is impossible to establish any unified formalism for it. Brain essentially is a part of the living, not “bio-logic” but fully biological reality, which has an emergent mode of existence with its unique properties. Such processes as neuroplasticity and synaptic pruning are responsible for adaptive and teleonomic re‑shaping of the brain. Usual conventional logic is suitable for computing machines and their nets, but it is incapable of reproducing such autopoietic phenomena. As a result, there is a fatal ontological gap between natural networks of neurons and ANNs. The second ones are not able to implement complex cognitive functions as they are product of the logocentric hypertrophy of algorithms which is fully devoid of flexibility of the living brain.
Ethics of AI Technologies in “Sensitive” Content Creation and Evaluation. School Shooting Cases
Authors: Osipov Daniil V. 
Published: September 2024

This article looks into the ethical issues raised by AI-generated content, focusing on ‘sensitive’ topics like school shootings. As AI technologies progress, there is a greater risk that such information can accidentally reinforce negative narratives, glorify acts of violence, or cause psychological damage to victims and their communities. The study tackles these concerns by estimating the existing ethical frameworks and finding their limitations in dealing with these complicated situations. A main goal of the research is to create a refined set of ethical principles specifically geared to address the risks connected with AI-generated information about school shootings. The paper contains actual experiments using AI models such as ChatGPT, Claude, GigaChat, and YandexGPT to generate and analyze information about school shootings. These experiments highlight important issues in ensuring that AI-generated texts do not reinforce negative themes or cause suffering. For example, while some models, such as GigaChat, declined to generate content on sensitive themes, others, such as ChatGPT, created elaborate texts that risked retraumatizing readers or praising offenders. The findings show that, while current frameworks take into consideration basic concepts such as transparency, accountability, and fairness, they frequently lack precise direction for dealing with difficult issues. To close this gap, the suggested ethical framework incorporates particular content development criteria, stakeholder participation, responsible dissemination techniques, and ongoing research. This paradigm prioritizes the protection of vulnerable people and the prevention of psychological injury.