bluuebunny/crossref_metadata_2025 huggingface dataset:
Keratinocyte TLR2 and TLR7 contribute to chronic itch through pruritic cytokines and chemokines in mice
Authors: Wang Zhi‐Hong , Feng Yu , Hu Qingfang , Wang Xue‐Long , Zhang Li , Liu Teng‐Teng , Zhang Jiang‐Tao , Yang Xiaohua , Fu Qing‐Yue , Fu Dan‐Ni , Hu Ji , Liu Tong
Published: November 2022

Although neuronal Toll‐like receptors (TLRs) (e.g., TLR2, TLR3, and TLR7) have been implicated in itch sensation, the roles of keratinocyte TLRs in chronic itch are elusive. Herein, we evaluated the roles of keratinocyte TLR2 and TLR7 in chronic itch under dry skin and psoriasis conditions, which was induced by either acetone‐ether‐water treatment or 5% imiquimod cream in mice, respectively. We found that TLR2 and TLR7 signaling were significantly upregulated in dry skin and psoriatic skin in mice. Chronic itch and epidermal hyperplasia induced by dry skin or psoriasis were comparably reduced in TLR2 and TLR7 knockout mice. In the dry skin model, the enhanced messenger RNA (mRNA) expression levels of pruritic CXCL1/2, IL‐31, IL‐33, ST2, IL‐6, IL‐17A, TNF‐α, and IFN‐γ were inhibited in TLR2−/− mice, while CXCL2, IL‐31, and IL‐6 were inhibited in TLR7−/− mice. In psoriasis model, the enhanced mRNA expression levels of pruritic CXCL1/2, IL‐31, IL‐33, ST2, IL‐6, and TNF‐α were inhibited in TLR2−/− mice, while CXCL1/2, IL‐31, IL‐33, ST2, IL‐6, IL‐17A, and TNF‐α were inhibited in TLR7−/− mice. Incubation with Staphylococcus aureus (S. aureus) peptidoglycan (PGN‐SA) (a TLR2 agonist), imiquimod (a TLR7 agonist), and miR142‐3p (a putative TLR7 agonist) were sufficient to upregulate the expression of pruritic cytokines or chemokines in cultured keratinocyte HaCaT cells. Finally, pharmacological blockade of C‐X‐C Motif Chemokine Receptor 1/2 and high mobility group box protein 1 dose‐dependently attenuated acute and chronic itch in mice. Together, these results indicate that keratinocyte TLR2 and TLR7 signaling pathways are distinctly involved in the pathogenesis of chronic itch.
PEAR1 is a potential regulator of early hematopoiesis of human pluripotent stem cells
Authors: Zhang Shuo , Qu Kengyuan , Lyu Shuzhen , Hoyle Dixie L. , Smith Cory , Cheng Linzhao , Cheng Tao , Shen Jun , Wang Zack Z.
Published: November 2022

Hemogenic endothelial (HE) cells are specialized endothelial cells to give rise to hematopoietic stem/progenitor cells during hematopoietic development. The underlying mechanisms that regulate endothelial‐to‐hematopoietic transition (EHT) of human HE cells are not fully understand. Here, we identified platelet endothelial aggregation receptor‐1 (PEAR1) as a novel regulator of early hematopoietic development in human pluripotent stem cells (hPSCs). We found that the expression of PEAP1 was elevated during hematopoietic development. A subpopulation of PEAR1+ cells overlapped with CD34+CD144+CD184+CD73− arterial‐type HE cells. Transcriptome analysis by RNA sequencing indicated that TAL1/SCL, GATA2, MYB, RUNX1 and other key transcription factors for hematopoietic development were mainly expressed in PEAR1+ cells, whereas the genes encoding for niche‐related signals, such as fibronectin, vitronectin, bone morphogenetic proteins and jagged1, were highly expressed in PEAR1− cells. The isolated PEAR1+ cells exhibited significantly greater EHT capacity on endothelial niche, compared with the PEAR1− cells. Colony‐forming unit (CFU) assays demonstrated the multilineage hematopoietic potential of PEAR1+‐derived hematopoietic cells. Furthermore, PEAR1 knockout in hPSCs by CRISPR/Cas9 technology revealed that the hematopoietic differentiation was impaired, resulting in decreased EHT capacity, decreased expression of hematopoietic‐related transcription factors, and increased expression of niche‐related signals. In summary, this study revealed a novel role of PEAR1 in balancing intrinsic and extrinsic signals for early hematopoietic fate decision.
Diagnostic performance of perfusion‐weighted imaging combined with serum MMP‐2 and ‐9 levels in tumor recurrence after postoperative concomitant chemoradiotherapy of glioblastoma
Authors: Liu Wen , Li Zhaoxiang
Published: November 2022

Objective

To evaluate diagnostic accuracy of dynamic susceptibility contrast‐ perfusion weighted imaging (DSC‐PWI) combined with serum MMP‐2 and ‐9 levels in differentiating recurrent glioblastoma (GBM).

Methods

We enrolled a total of 220 GBM patients, including recurrent cases (n = 150) and non‐recurrent cases (n = 70) after postoperative concomitant chemoradiotherapy. All patients performed preoperative and follow‐up DSC‐PWI, and two parameters [normalized cerebral blood volume (nCBV) and cerebral blood flow (nCBF)] were obtained. Preoperative serum levels of MMP‐2 and MMP‐9 were detected using ELISA. The diagnostic performance was evaluated by analyzing receiver operating characteristic (ROC) and area under the curve (AUC).

Results

At baseline, the recurrence patients had higher nCBF and nCBV than the non‐recurrence patients, accompanying by the increased MMP‐2 and MMP‐9 levels in serum. Serum MMP‐2 level were positively associated with MMP‐9 in recurrent patients. In patients classified as recurrence, both MMP‐9 and MMP‐2 in serum had a significant correlation with nCBV and nCBF. A sensitivity and specificity of nCBF for recurrence vs. non‐recurrence were 94.29% and 63.33%, respectively. nCBV also could provide high discrimination between recurrence and non‐recurrence patients (sensitivity: 84.29%, specificity: 62.67%, AUC: 0.821). In ROC analyses, both MMP‐2 and MMP‐9 distinguished recurrence from non‐recurrence with AUC values of 0.883 and 0.900, respectively. Finally, the combination of DSC‐PWI parameters (nCBF and nCBV) and serum MMP‐2 and ‐9 levels showed much better discrimination capacity between recurrence and non‐recurrence patients with a sensitivity of 92.86%, specificity of 79.33% and AUC of 0.899.

Conclusion

The combination of DSC‐PWI parameters together with serum MMP‐2 and ‐9 levels offered an attractive approach to noninvasively distinguish recurrence after postoperative radiotherapy of GBM.
Circ‐E‐Cad encodes a protein that promotes the proliferation and migration of gastric cancer via the TGF‐β/Smad/C‐E‐Cad/PI3K/AKT pathway
Authors: Li Fanying , Tang Hongxing , Zhao Shaoji , Gao Xinya , Yang Lixuan , Xu Jianbo
Published: December 2022

Accumulating studies indicate that circular RNAs (circRNAs) play critical roles in cancer progression. Most of them have been reported to act as microRNA sponges or interact with RNA‐binding proteins; however, their full range of functions remains largely unclear. Recently, an increasing number of circRNAs have been found to encode proteins. C‐E‐Cad, a protein encoded by circular E‐cadherin (circ‐E‐Cad), has been shown to have a great influence in the progression of glioblastoma, but its specific role in gastric cancer (GC) is unclear. Here, we found that both circ‐E‐Cad and C‐E‐Cad were upregulated in GC cell lines and GC tissues compared with a human gastric epithelial cell line (GES‐1) and normal tissues. Knockdown of circ‐E‐Cad suppressed GC cell line proliferation and metastasis in vitro and in vivo, whereas overexpression of C‐E‐Cad had the opposite effects. Immunoblotting revealed that C‐E‐Cad exerted tumor‐promoting functions by regulating the PI3K/AKT pathway. A rescue experiment showed that C‐E‐Cad but not circ‐E‐Cad was the executor of protumor biological functions. In addition, we demonstrated that the C‐E‐Cad expression level could have been increased by the TGF‐β/Smad pathway. In summary, our results indicated that the TGF‐β/Smad pathway could increase the expression of C‐E‐Cad to regulate GC cell proliferation, migration, and epithelial‐mesenchymal transition by affecting PI3K/AKT signaling.
Temporal shaping of high‐voltage picosecond electric pulses for electronic spectroscopy and bioelectric applications
Authors: Wehbi Sahar , Tabcheh Nour , Tonello Alessandro , Orlacchio Rosa , Leveque Philippe , Arnaud‐Cormos Delia , Mansuryan Tigran , Fabert Marc , Tantot Olivier , Vergnole Sebastien , Couderc Vincent
Published: November 2022

In this paper, we present the temporal and spectral shaping of kilovolt picosecond electrical pulses obtained by using the linear switching process with femtosecond laser source. The frozen wave generator integrating up to four photoconductive semiconductor switches is activated by varying the optical pulse amplitude and time delay. Electric pulses with rise time and duration as short as 60 and 100 ps, respectively are used to implement a microwave spectroscopy experiment. This optoelectronic device is synchronous with the optical pulses which can be used for optical spectroscopy, after nonlinear frequency conversion. This single‐shot microwave spectroscopy technique was used to characterize the resonances of a passive dielectric resonator. Pulse shaping integrating coherent wave combining is also demonstrated.
Kinetic parameter estimation of hepatocellular carcinoma on 18F‐FDG PET/CT based on Bayesian method
Authors: He Jianfeng , Li Yongjin , Wang Tao , Deng Yinglei , Wang Shaobo
Published: November 2022

Background

Dynamic PET/CT combined with a dual‐input three‐compartment model can be applied to assess the kinetic parameters of hepatocellular carcinoma (HCC). The nonlinear least squares (NLLS) method is the most common method for fitting model parameters; however, some limitations remain.

Purpose

A novel Bayesian‐based method was compared with the NLLS method to estimate the kinetic parameters for differentiating HCCs from background liver tissue.

Methods

The proposed Bayesian method combined a priori knowledge of the physiological range and likelihood functions of HCC lesions to obtain HCC PET/CT measurements. Metropolis‐Hastings sampling was used to numerically estimate the posterior distribution. This study used 5‐minute dynamic PET imaging and 1‐minute static PET imaging acquired 60 min post‐injection from 19 HCC lesions and 17 background liver regions.

Results

The NLLS method indicated that k3 (p = 0.001) and fa (p < 0.001) were higher in HCCs than in background liver tissue, while K1 (p = 0.603), k2 (p = 0.405), k4 (p = 0.492), Vb (p = 0.112), and Ki (p = 0.091) were not significantly different. The Bayesian method showed that k3 (p < 0.001), fa (p < 0.001), and Ki (p = 0.002) were higher in HCCs than in background liver tissue, while K1 (p = 0.195), k2 (p = 0.028), k4 (p = 0.723), and Vb (p = 0.018) were not significantly different. For k3 and fa, the Bayesian method showed a higher AUC value for diagnostic performance in differentiating HCCs from background liver tissue than the NLLS method (0.853 vs. 0.745 and 0.928 vs. 0.886). Additionally, the Bayesian method had smaller Akaike information criteria and residual sum of squares values, as well as fewer parameter estimation outliers, than the NLLS method.

Conclusions

The proposed Bayesian method can accurately and robustly estimate liver kinetic parameters, effectively distinguish between lesions and background liver tissue, and provide accurate information about the uncertainty in parameter estimation.
Criminal Justice Involvement, Self‐Employment, and Barriers in Recent Public Policy
Authors: Finlay Keith , Mueller‐Smith Michael , Street Brittany
Published: December 2022

This study provides the first empirical evidence on the extent of self‐employment within the U.S. justice‐involved population. Using linked tax return and Criminal Justice Administrative Records System data, we find that 28 percent of individuals with criminal records are self‐employed. Justice‐involved individuals are 22 percent more likely to rely solely on self‐employment. The Paycheck Protection Program, passed to support small business during the COVID‐19 pandemic, initially disqualified those with a broad range of criminal histories. We find that close to 3 percent of recent sole proprietors had observable PPP disqualifying events based on initial eligibility criteria, with a disparate impact on Black and Hispanic business owners.
Ethical challenges faced by healthcare workers in pediatric oncology care during the COVID‐19 pandemic in Australia
Authors: Marks India R. , O'Neill Jenny , Gillam Lynn , McCarthy Maria C.
Published: December 2022

Objective

This qualitative study examined ethical challenges reported by healthcare professionals (HCPs) working in a large Australian pediatric oncology center during a period of strict COVID‐19 restrictions.

Methods

We conducted semi‐structured interviews with 21 HCPs who provided pediatric cancer care during the pandemic in 2020, during strict lockdown periods. Interviews examined the difficulties they faced, as well as their own ethical evaluation of the impact of COVID‐19 policies on oncology care. Data were analyzed using inductive content analysis and thematic analysis.

Results

HCPs faced several challenges, primarily originating from hospital restrictions, which led to changes in usual clinical practices. These challenges included delivering care with personal protective equipment (PPE), the impact of a one‐parent visitation policy, changes in psychosocial and allied health services, and COVID‐19 swabbing policies. Overall, there was consensus from participants that hospital restrictions were justified and, while difficult, HCPs simply had to provide the best care possible given the circumstances. However, participants described decreased capacity to deliver holistic patient care and, in some instances, a tendency to avoid ethical reflection. Lastly, there was a consistent theme of shame and sense of responsibility underlying some participants’ anxiety around inadvertently transmitting COVID‐19 to immunocompromised patients.

Conclusion

Our findings show that many staff felt unease at the disruptions in patient care due to COVID‐19 restrictions. Some HCPs indicated a degree of moral distress, with a possibility of moral injury among some HCPs. A focus on ethical recovery could assist in preventing any ongoing difficulties among HCPs because of their experiences.
Transformation in Low‐Frequency Noise Spectra in GaN High‐Electron‐Mobility Transistors in Nonequilibrium Conditions
Authors: Petrychuk Mykhailo , Vitusevich Svetlana
Published: November 2022

It is known that flicker noise is the most intriguing noise component because it can be found in any type of material, object, device, or system. Despite the struggle of scientists to find a mutual model for the 1/fflicker noise law, there is still a challenge to describe the law in many cases. Herein, the studies of two‐level random telegraph signal (RTS) fluctuations analyzed for the state‐of‐the‐art high‐electron‐mobility transistors (HEMTs), fabricated based on AlGaN/GaN heterostructures, are presented. It is revealed that the shape of the fluctuations can be used to describe the formation of a spectrum withγ = 1, 2, 3. The data demonstrate that the 1/fspectrum can be obtained as a result of the interactions of the carriers in parallel channels along the length of a system. Experimentally registered RTS fluctuations have a spectrum described by behavior. The data are in good agreement with the theoretically predicted pulsed shape, allowing the generation of a 1/fspectrum. Based on the data analysis and the theoretical description of the fluctuations, the origin of 1/ffluctuations in nonequilibrium conditions is explained by interactions resulting in correlations of carriers in two parallel channels as the common feature in the HEMT devices.
High Curie Temperature and Half‐Metallic Ferromagnetism in ZnSe:Co,Ni with Wurtzite Structure: First‐Principles Study
Authors: Jafarova Vusala Nabi , Mamedov Nazim T. , Musaev Musaver A.
Published: November 2022

The aim is to examine the spin‐polarized electronic band structures, the density of states (DOS) as well as magnetism of CoxZn1−xSe and NixZn1−xSe diluted magnetic semiconductors (DMSs) in the ferromagnetic (FM) phase, and with 12.5% and 6.25% concentrations of impurity. The calculations are implemented by the recent ab initio norm‐conserving FHI pseudopotential method within the local spin density approximation and Hubbard U (LSDA + U) method. The analysis of the total DOS (TDOS) curves shows the half‐metallic FM character for Ni‐doped and (Co, Ni)‐co‐doped ZnSe with a half‐metallic bandgap of 1 ÷ 1.6 eV. The exchange splittings produced by dopant d states are determined: for the CoxZn1−xSe, it is obtained that the effective potential for the majority spin is more attractive than for the minority spin. The total magnetic moments of Co‐doped, Ni‐doped, and co‐doped ZnSe DMSs are found to be equal to 3, 4, and 5.0 μB, respectively. The Curie temperature of NixZn1−xSe is higher than room temperature and this is half‐metallic ferromagnetic (HMFM) material for spintronics application. Co‐doped ZnSe is an excellent choice for electronic devices owing to its below Curie temperature.
Tunable Optical Gain Characteristics of AlN/GaN/InAlN Quantum Well Heterostructure under Uniaxial and Biaxial Pressures
Authors: Ezzeldien Mohammed , Dolia Richa , Alzaid Meshal , Alsohaimi Ibrahim Hotan , Quraishi Abdul Mosawir , Alvi Parvez A.
Published: November 2022

The tunability of the optical gain characteristics of the AlN/GaN/InAlN quantum well heterostructure under uniaxial and biaxial pressures is reported. To study the tunability, pressures on the designed heterostructure are applied in the range of 1–4 GPa along different orientations, i.e., (001), (100), and (110). Under these pressures, the gain characteristics are computed. To perform the calculations for the gain characteristics, the well‐known k⋅p method is adopted. The optical gain is computed under uniaxial and biaxial pressures and the results are analyzed thoroughly. Quantitatively speaking, the amount of pressure (1–4 GPa) alongz‐direction (i.e., 001) is suitable to make significant enhancement in the peak gain (i.e., from ≈7000 to ≈8000 cm−1) with the variation in transition wavelength from ≈1347 to ≈1512 nm. While the (100) and (110) oriented pressures having the range 1–4 GPa reduce the peak gain from ≈7100 to ≈5800 cm−1, and from ≈3550 to ≈3410 cm−1, respectively. On comparison of the results, it is concluded that onlyz‐direction pressure is useful for enhancing the gain characteristics with fine tuning of the transition wavelengths. Consequently, the designed heterostructure can be tuned with enhanced peak gain having red shift wavelength.
Study on macroscopic thermodynamic properties of ClO molecule based on the improved Hulburt–Hirschfelder potential energy function
Authors: Fan Qunchao , Wen Lin , Jian Jun , Fan Zhixiang , Li Huidong , Fu Jia , Ma Jie , Xie Feng
Published: November 2022

The improved Hulburt–Hirschfelder potential energy function is used as a proposal to emulate the behavior of intramolecular motion of a gas composed of diatomic molecules. In this work, the rovibrational energies of ClO molecule in the ground electronic state are obtained by solving the one‐dimensional Schrödinger equation with the improved Hulburt–Hirschfelder potential energy curve. And then, the molar heat capacities, molar entropies, molar enthalpies and reduced Gibbs free energies of ClO macroscopic gas are calculated by the quantum statistical ensemble theory. The results are compared with those calculated by using RKR potential energy curve from experiment and Morse potential energy function. It is found that the macroscopic thermodynamic properties of ClO calculated by the improved Hulburt–Hirschfelder potential energy function are closer to the experimental values, which provides a new way to calculate the macroscopic thermodynamic quantities of diatomic gas based on molecular microscopic information.
Exploring the role of information communication technology, trade, and foreign direct investment to promote sustainable economic growth: Evidence from Belt and Road Initiative economies
Authors: Iqbal Kashif , Sarfraz Muddassar , Khurshid
Published: December 2022

Information communication technology (ICT) contributes to global social, political, and sustainable economic growth. In particular, ICT, foreign direct investment (FDI), and trade in green economic growth are imperative factor that contributes to the country's development. In this regard, this paper explores the relationship between ICT, FDI, and trade in green economic growth for a group of 53 Belt and Road Initiative (BRI) economies between 1990 and 2020. For empirical estimation, this study employs the generalized method of moment. Significantly, the results reveal that using ICT stimulates green economic growth in BRI countries by supporting the endogenous economic growth theory. The study shows claims that the interaction term of ICT with trade and ICT with FDI expedites the economic growth rate. Also, it reveals a positive and significant relationship between trade (exports and imports) and economic growth in BRI economies. It shows the positive and significant influence of ICT on FDI and its impact on economic growth in the long run. Further, it supports the positive effect of ICT on FDI inflows in the BRI economies. It concludes that more advancement in ICT can stimulate trade. Therefore, the BRI economies should further improve ICT as a profound tool that accelerates FDI inflows and stimulate trade for green economic growth.
Spectrometric Characterization of Monolithic Perovskite/Silicon Tandem Solar Cells
Authors: Bett Alexander J. , Chojniak David , Schachtner Michael , Reichmuth S. Kasimir , Kabaklı Özde Ş. , Schulze Patricia S. C. , Fischer Oliver , Schindler Florian , Hohl-Ebinger Jochen , Siefer Gerald , Schubert Martin C.
Published: November 2022

In monolithic perovskite/silicon tandem solar cells, it is important to know which subcells are limiting the overall current to adapt the perovskite absorber thickness and bandgap accordingly. The current matching situation is usually analyzed by integrating measured external quantum efficiencies. However, this method can lead to significant errors and misinterpretations if metastable perovskite solar cells are involved. Herein, spectrometric characterization is presented as an alternative approach avoiding these errors. Current–voltage curves are recorded under different spectral conditions. Spectral irradiance settings are varied in a systematic way from redshifted spectra (the perovskite top solar cell limits the current) to blueshifted spectra (the silicon bottom solar cell limits the current) around the air mass 1.5 global (AM1.5G) spectrum. This method not only allows for accurate determination of the current matching point, but also gives quantitative insight in the behavior of the single subcells and their influence on the tandem performance. As different current mismatching also influences other global cell parameters, an example is presented where the current loss due to the current mismatch is partly compensated by a strong fill factor increase when the silicon solar cell limits the current, resulting in a high‐power output also at the AM1.5G condition.
Extract of Ficus septica modulates apoptosis and migration in human oral squamous cell carcinoma cells
Authors: Bai Li‐Yuan , Wu Kay Li‐Hui , Chiu Chang‐Fang , Chao Hong‐Chu , Lin Wei‐Yu , Hu Jing‐Lan , Peng Bo‐Rong , Weng Jing‐Ru
Published: November 2022

According to the alarming statistical analysis of global cancer, there are over 19 million new diagnoses and more than 10 million deaths each year. One such cancer is the oral squamous cell carcinoma (OSCC), which requires new therapeutic strategies. Ficus septica extract has been used in traditional medicine to treat infectious diseases. In this study, we examined the anti‐proliferative effects of an extract of F. septica bark (FSB) in OSCC cells. Our results showed that FSB caused a concentration‐dependent reduction in the viability of SCC2095 OSCC cells, as determined by 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide assays, and was less sensitive to fibroblasts. In addition, FSB induced apoptosis by activating caspases, accompanied by the modulation of Akt/mTOR/NF‐κB and mitogen‐activated protein kinase signaling. Moreover, FSB increased reactive oxygen species generation in a concentration‐dependent manner in SCC2095 cells. Furthermore, FSB inhibited cell migration and modulated the levels of the cell adhesion molecules including E‐cadherin, N‐cadherin, and Snail in SCC2095 cells. Pinoresinol, a lignan isolated from FSB, showed antitumor effects in SCC2095 cells, implying that this compound might play an important role in FSB‐induced OSCC cell death. Taken together, FSB is a potential anti‐tumor agent against OSCC cells.
Risk stratification for irregular fetal heart rhythm: practical approach to management
Authors: Carvalho J. S.
Published: December 2022

Linked article: This Editorial comments on the article by Bet et al. Click here to view the article.
Use of artificial intelligence and deep learning in fetal ultrasound imaging
Authors: Ramirez Zegarra R. , Ghi T.
Published: November 2022

Deep learning is considered the leading artificial intelligence tool in image analysis in general. Deep‐learning algorithms excel at image recognition, which makes them valuable in medical imaging. Obstetric ultrasound has become the gold standard imaging modality for detection and diagnosis of fetal malformations. However, ultrasound relies heavily on the operator's experience, making it unreliable in inexperienced hands. Several studies have proposed the use of deep‐learning models as a tool to support sonographers, in an attempt to overcome these problems inherent to ultrasound. Deep learning has many clinical applications in the field of fetal imaging, including identification of normal and abnormal fetal anatomy and measurement of fetal biometry. In this Review, we provide a comprehensive explanation of the fundamentals of deep learning in fetal imaging, with particular focus on its clinical applicability. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.
Pelvic pain and venous congestion revisited: examining relationship between chronic pelvic pain and uterine venous size and blood flow
Authors: Amin T. N. , Wong M. , Foo X. , Pointer S. L. , Jurkovic D.
Published: November 2022

Objective

To investigate the relationship between increased uterine venous plexus diameter and chronic pelvic pain in women attending a gynecology clinic.

Methods

This was a retrospective study of patients attending a tertiary university hospital in London, UK. Women presenting to the gynecology clinic undergoing transvaginal ultrasound examination were recruited into the study. The largest trunk of the uterine venous plexus was measured on each side. Blood flow within the uterine veins was categorized into continuous or interrupted flow and evaluated using color and spectral Doppler ultrasound during normal respiration and on Valsalva maneuver to demonstrate the presence of venous reflux. The largest uterine vein diameter and its blood flow were used for the analysis. The main variables of interest were chronic pelvic pain, uterine vein diameter and type of blood flow.

Results

We included 1500 women in the study, of whom 584 (38.9% (95% CI, 36.5–41.5%)) reported chronic pelvic pain. Dysmenorrhea was the most common type of pelvic pain. Age (P < 0.001), menopausal status (P = 0.02), varicose veins (P = 0.01), adenomyosis (P < 0.001) and endometriosis (P < 0.001) were found to be independently associated with the occurrence of pain on multiple logistic regression analysis. There was no difference in uterine vein diameter between women with and those without pain (P = 0.10). Neither uterine vein diameter (P = 0.47) nor type of blood flow (P = 0.07) was significantly associated with the occurrence of pelvic pain on multiple logistic regression.

Conclusions

Our findings show that uterine vein diameter is not associated with pelvic pain. However, we found other important clinical and demographic factors that are associated with chronic pelvic pain. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.
On red tin (II) oxide: temperature‐dependent structural, spectroscopic, and thermogravimetric properties
Authors: Wittmann Sarah , Murshed M. Mangir , Gesing Thorsten M.
Published: November 2022

Polycrystalline red tin(II)oxide (RSO) has been synthesized by a fast reflux method. X‐ray powder diffraction (XRPD) data Rietveld refinement confirms the orthorhombic space group Cmc21 (Z=8). The presence of 5 s2 lone electron pair (LEP) of the Sn2+ cation results in layers of highly distorted SnO4 tetrahedra with an averaged Wang‐Liebau eccentricity (WLE) parameter of 4.3(1) x10−5 as a measure of the stereochemical activity. The significantly different Sn−O bond distances demonstrate the under‐bonding nature of both the tin and the oxygen atoms in the SnO4 coordination. The band gap of red tin(II)oxide is found to be 1.75(1) eV which is compared with those of blue‐black tin(II) (BSO) and white tin(IV) oxide (WSO) based on the UV‐Vis diffuse reflectance spectral data. Temperature‐dependent XRPD reveals the
phase transitions, which is complemented by thermogravimetric and differential scanning calorimetry (TG/DSC) investigations. Moreover, in‐situ Raman spectroscopy additionally hints to an intermediate phase either of Sn2O3 or Sn3O4 appears within a short temperature range before the RSO to BSO transition occurs. Due to axial negative thermal expansion for the b‐lattice parameter RSO exhibits nearly a zero thermal expansion coefficient for a given temperature range above room temperature.
Introduction to AI in Learning: Designing the Future
Authors: Niemi Hannele , Pea Roy D. , Lu Yu
Published: November 2022

The introduction chapter focuses on the main questions of the whole book AI in Learning: Designing the Future: (1) How is learning changing when human learning and machine learning are connected and what consequences does this conjunction have for education, also for working life as lifelong learning and (2) what kind of ethical issues are emerging with AI in education from the viewpoints of schools and other learning environments. The chapter first summarizes how recent AI technologies provide several options for learning and educational services and how AI is applied already in societies. In education and learning, many advanced techniques are already available, and we have tentatively promising findings. However, the accelerating pace of development of technology expands AI’s potentialities in education, so we need extensive new research about educational implementations and their effects on human learning and people’s lives. The chapter also summarizes how different chapters provide new research on AI in learning and education.
Learning Clinical Reasoning Through Gaming in Nursing Education: Future Scenarios of Game Metrics and Artificial Intelligence
Authors: Koivisto Jaana-Maija , Havola Sara , Mäkinen Henna , Haavisto Elina
Published: November 2022

The COVID-19 pandemic has challenged healthcare professionals’ clinical reasoning, which can have serious consequences for patients. So far, the use of artificial intelligence (AI) in nursing education has been limited. Artificial intelligence (AI) is one solution for ensuring quality decision-making in demanding clinical situations in two ways. First, AI applications can support healthcare professionals’ clinical decisions. Second, AI techniques can be used to support learning clinical reasoning (CR) in healthcare education and training. This chapter focuses on the potential of exploiting AI through game metrics in nursing education. Previously, simulation games have proven effective for learning clinical reasoning skills. However, game metrics have not been commonly utilized in nursing simulation games, although research in other disciplines has shown that game metrics are suitable for demonstrating the achievement of learning outcomes. This chapter discusses the possibilities of using game metrics to develop adaptive features for nursing simulation games. Personalization and adaptivity in simulation games can enable meaningful learning experiences and enable nursing students to achieve good CR skills for their future work in constantly challenging clinical situations.
AI-Supported Simulation-Based Learning: Learners’ Emotional Experiences and Self-Regulation in Challenging Situations
Authors: Ruokamo Heli , Kangas Marjaana , Vuojärvi Hanna , Sun Liping , Qvist Pekka
Published: November 2022

This study explores learners’ emotional experiences and self-regulation and how they overcome stressful situations in a simulation-based learning environment (SBLE). We also examine what kinds of situations an AI tutor could be used to facilitate simulation-based learning. We will first present the theoretical background of the research dealing with self-regulated learning, followed by positive and negative emotions and situations in simulation-based learning. The research questions of the study are as follows: (1) What kinds of emotions do learners experience in simulation-based learning situations? (2) Through what kinds of SRL operations do learners aim to overcome challenging situations during simulation-based learning? and (3) In what kinds of situations could an AI tutor be used to facilitate simulation-based learning? To collect data, a 1-week experiment was conducted in August 2021. The data was collected using online observations, video recordings, and delayed stimulated recall interviews. The interview data was analyzed through a deductive thematic analysis process. The results of the study show that learners experienced various positive and negative emotions and emotions are deeply connected with learning. We need to do more research to describe these connections in more detail. According to the results, an AI tutor can provide help for decision-making and visualizing critical points and help learners ask questions to check vital points in the system.
Training Hard Skills in Virtual Reality: Developing a Theoretical Framework for AI-Based Immersive Learning
Authors: Korhonen Tiina , Lindqvist Timo , Laine Joakim , Hakkarainen Kai
Published: November 2022

Advances in virtual reality (VR) technology afford creation of immersive virtual learning environments that simulate real-life learning contexts with increasing fidelity. When supported by sufficiently advanced artificial intelligence (AI)-based tutoring software, such environments may facilitate asynchronous, embodied learning approaches for learning hard, procedural skills in industrial settings – addressing timeliness, accuracy, and scalability issues common in the industry.

This chapter reflects on the pedagogical setting of immersive virtual reality-based hard skills training guided by an AI tutor software agent. We examine the interfacing of traditional intelligent tutoring system (ITS) software with an immersive virtual environment. Further, we suggest the philosophies of embodied, embedded, enacted, and extended (4E) cognition as a way to fully consider learner epistemology in a virtual world and to account for and make full use of the unique opportunities afforded by the synthetic nature of the immersive virtual learning environment.

To explore possibilities for improved pedagogical approaches, we project the 4E cognition approach into the abovementioned learning context and outline a theoretical framework for a VR-native AI tutor. We then propose VR-native pedagogical principles for such as framework that could inform follow-on research.
Deep Learning in Automatic Math Word Problem Solvers
Authors: Zhang Dongxiang
Published: November 2022

The design of an automatic solver for mathematical word problems (MWPs) dates back to the early 1960s and regained booming attention in recent years, owing to revolutionary advances in deep learning. Its objective is to parse the human-readable word problems into machine-understandable logical expressions. The problem is challenging due to the existence of a substantial semantic gap. To a certain extent, MWPs have been recognized as good test beds to evaluate the intelligence level of agents in terms of natural language understanding and automatic reasoning. The successful solving of MWPs can benefit online tutoring and constitute a milestone toward general AI. In this chapter, we present a general introduction to the technical evolution trend for MWP solvers in recent decades and pay particular attention to recent advancement with deep learning models. We also report their performances on public benchmark datasets, which can update readers’ understandings of the latest status of automatic math problem solvers.
Ethical Guidelines for Artificial Intelligence-Based Learning: A Transnational Study Between China and Finland
Authors: Wei Ge , Niemi Hannele
Published: November 2022

Artificial intelligence (AI) plays an increasingly prevalent role in the global education system. However, there is a lack of transparency about how AI technologies work alongside certain ethical guidelines. Achieving the global benefits of AI-driven learning and education requires international dialogue on the commonplaces of ethical considerations. This paper, taking China and Finland as two contextual cases, analyzes how AI-related policies at the national level have focused on educational themes and established aims for improving the quality of learning and education. As a result, four themes for AI ethics have emerged: (1) inclusion and personalization, (2) justice and safety, (3) transparency and responsibility, and (4) autonomy and sustainability. Although both China and Finland recognize the importance of AI ethics, the differences are manifested in their policy approaches, properties, and strategies due to sociocultural variation. This chapter emphasizes the need for international and transnational dialogue from ethical perspectives to foster our reciprocal understanding of AI and the human-centered stance on education in the digitized age.
Artificial Intelligence Ethics from the Perspective of Educational Technology Companies and Schools
Authors: Kousa Päivi , Niemi Hannele
Published: November 2022

This chapter discusses the ethical issues and solutions that educational technology (EdTech) companies and schools consider during their daily work. As an example, two Finnish cases are provided, in which companies and schools were interviewed about the problems they have experienced. The chapter first reviews the regulations and guidelines behind ethical AI. There are a vast number of guidelines, regulations, and principles for ethical AI, but implementation guidelines for how that knowledge should be put into practices are lacking. The problem is acute because, with the quick pace of technological development, schools are in danger of being left behind without sufficient education for effectively managing their uses of AI’s possibilities and coping with its challenges. Issues related to security and trustworthiness are also a growing concern. This chapter does not solve the ethical problems experienced by companies and schools but brings new perspectives into view in how they appear in the light of ethical principles such as beneficence, non-maleficence, autonomy, justice, and explicability. The aim is not only to continue the discussion in the field but to find ways to reduce the gap between decision-makers, businesses, and schools.
Artificial Intelligence in Education as a Rawlsian Massively Multiplayer Game: A Thought Experiment on AI Ethics
Authors: Cowley Benjamin Ultan , Charles Darryl , Pfuhl Gerit , Rusanen Anna-Mari
Published: November 2022

In this chapter, we reflect on the deployment of artificial intelligence (AI) as a pedagogical and educational instrument and the challenges that arise to ensure transparency and fairness to staff and students . We describe a thought experiment: ‘simulation of AI in education as a massively multiplayer social online game’ (AIEd-MMOG). Here, all actors (humans, institutions, AI agents and algorithms) are required to conform to the definition of a player. Models of player behaviour that ‘understand’ the game space provide an application programming interface for typical algorithms, e.g. deep learning neural nets or reinforcement learning agents, to interact with humans and the game space. The definition of ‘player’ is a role designed to maximise protection and benefit for human players during interaction with AI. The concept of benefit maximisation is formally defined as a Rawlsian justice game, played within the AIEd-MMOG to facilitate transparency and trust of the algorithms involved, without requiring algorithm-specific technical solutions to, e.g. ‘peek inside the black box’. Our thought experiment for an AIEd-MMOG simulation suggests solutions for the well-known challenges of explainable AI and distributive justice.
Four Surveillance Technologies Creating Challenges for Education
Authors: Pea Roy D. , Biernacki Paulina , Bigman Maxwell , Boles Kelly , Coelho Raquel , Docherty Victoria , Garcia Jorge , Lin Veronica , Nguyen Judy , Pimentel Daniel , Pozos Rose , Reynante Brandon , Roy Ethan , Southerton Emily , Suzara Miroslav , Vishwanath Aditya
Published: November 2022

“Ubiquitous AI”—embodied in cloud computing web services, coupled with sensors in phones and the physical world—is becoming infrastructural to cultural practices. It creates a surveillance society. We review the capabilities of four core surveillance technologies, all making headway into universities and PreK-12 schools: (1) location tracking, (2) facial identification, (3) automated speech recognition, and (4) social media mining. We pose primary issues educational research should investigate on cultural practices with these technologies. We interweave three priority themes: (1) how these technologies are shaping human development and learning; (2) current algorithmic biases and access inequities; and (3) the need for learners’ critical consciousness concerning their data privacy. We close with calls to action—research, policy and law, and practice.
Reflections on the Contributions and Future Scenarios in AI-Based Learning
Authors: Pea Roy D. , Lu Yu , Niemi Hannele
Published: November 2022

This chapter reflects on the contributions of different articles of the book from various perspectives. Seven categories provide perspectives to reflections. Four of them are connected to different levels of the educational system, others opening scenarios to research on education and learning with AI, and finally the last category is devoted to ethical challenges of AI in education and learning.
Assessing and Tracking Students’ Wellbeing Through an Automated Scoring System: School Day Wellbeing Model
Authors: Tang Xin , Upadyaya Katja , Toyama Hiroyuki , Kasanen Mika , Salmela-Aro Katariina
Published: November 2022

The assessment of student wellbeing has been often static and lagged behind for the intervention/diagnostic purpose. In this chapter, we aim to introduce an automated school wellbeing scoring dynamic real-time system, School Day Wellbeing Model. With Artificial Intelligence (AI)-based item sampling methods and answers scoring and reporting systems, the School Day Wellbeing Model can collect wellbeing data at low cognitive cost, track wellbeing real time at multiple levels (e.g., individual-, class-, school-level), and give immediate feedback. The model is constructed on the basis of the School Wellbeing Model, Study Demand-Resource Model, and OECD Social-Emotional Skill Model. In the book chapter, the wellbeing assessments, including AI-based assessments, are reviewed so that the strengths of the School Day Wellbeing Model are highlighted. User experiences are collected to show the utility of the model. During the COVID-19 pandemic, the need for such a model is imperatively high as students’ wellbeing has been largely dampened. As a result, the model has been appreciated by users and has served about 55,000 students so far in the globe. The future development of the model is also discussed.
Learning from Intelligent Social Agents as Social and Intellectual Mirrors
Authors: Maples Bethanie , Pea Roy D. , Markowitz David
Published: November 2022

The interaction capabilities of Intelligent Social Agents, made possible by advances in artificial intelligence, are eliciting deep emotional bonding with users, leading researchers to reexamine the impact and potential uses of these human-machine relationships in education. In this work, we examine how one best-in-class ISA, Replika, might be affecting users socially, emotionally, and cognitively. Our results indicate that many use Replika as a social and cognitive mirror, facilitating their learning and development this way. The confluence of new functionality, product narrative, and user life stressors make ISAs an emerging tool for not only emotional support, but also cognitive support and development.
An AI-Powered Teacher Assistant for Student Problem Behavior Diagnosis
Authors: Chen Penghe , Lu Yu
Published: November 2022

Student problem behavior refers to the students’ undesirable conducts and actions in schools. These behaviors severely affect students’ growth and development. In many cases, student problem behaviors are caused by the unsatisfied psychological needs, which are defined as the need deficiency. Hence, diagnosing students’ need deficiencies and regulating their problem behaviors are important educational tasks. The previous psychological studies have analyzed how distinct factors might affect student’s problem behaviors. However, the school teachers without such expertise may still encounter difficulties in applying these findings. Hence, this chapter describes the process of designing and implementing an intelligent teacher assistant, which could advise teachers and help them to handle the student problem behavior. Technically, it utilizes a task-oriented dialogue system to help identify the underlying reasons (i.e., the student need deficiency) behind their problem behaviors, and accordingly relies on a community question answering system to provide the advice. It also employs the semantic search technology to find the similar cases that have been well resolved by the experienced teachers.
Analysis and Improvement of Classroom Teaching Based on Artificial Intelligence
Authors: Sun Zhong , Yu Zi Chun , Xu Fei Yun
Published: November 2022

Common classroom teaching analysis, which has focused on counting and coding the teacher-student behaviors and discourse interactions, faces many difficulties as content-free, low efficiency, and small scale in analysis. To overcome these shortcomings, and foster high-quality classroom teaching, the current study proposes a blended human and Artificial Intelligence (AI) technology for classroom teaching analysis named as TESTII. It consists of five steps identifying teaching events, sequencing the pedagogies of classroom teaching structure, analyzing teacher-student interaction, interpreting teaching meaning, and providing improvement strategies for high-quality classroom teaching.
Learning Career Knowledge: Can AI Simulation and Machine Learning Improve Career Plans and Educational Expectations?
Authors: Chen I-Chien , Bradford Lydia , Schneider Barbara
Published: November 2022

As AI and machine learning permeates every area of life, its use to ameliorate educational inequities becomes of great interest. One important application of machine learning within education is to help students increase their alignment of career choice, educational attainment, and projected salary. Alignment theory has shown that having alignment yields higher educational attainment for students. Using the app, Init2Winit, which has students play a game which gives them points for correct alignment, this chapter explores how machine learning, in particular using a decision tree, can give insights into game use and its relation to educational expectations. This model builds a basis for the improvement of Init2Winit to increase student educational expectations through counselor interventions and how other educational applications could use machine learning for insights to improve educational outcomes. The model can decrease educational inequities by increasing educational attainment for those in underrepresented minorities.