bluuebunny/crossref_metadata_2025 huggingface dataset:
Laparoscopic Anatomical Right Hepatectomy Using a Four-Incision Anterior Approach: Technical Details and Surgical Outcomes (with Video)
Authors: Liu Cong , Liu Haoling , Qi Le , Parra Maria A. , Bai Qingquan , Zou Jiashu , Cao Qian , Shen Xianbo , Yang Haiyan
Published: December 2023

Background
Laparoscopic hepatectomy, characterized by its complexity, poses distinct challenges compared to other laparoscopic procedures. However, with the continuous advancements in laparoscopic techniques, many surgeons have enhanced the feasibility and safety of this approach for carefully selected patients. The purpose of this study is to offer a comprehensive account of the technical aspects and surgical outcomes associated with laparoscopic anatomical right hepatectomy, specifically utilizing a four-incision anterior approach.
Patients and Methods:
A total of 9 patients with primary liver cancer located in the right lobe underwent laparoscopic anatomical right hepatectomy using a four-incision anterior approach. Detailed information regarding the surgical procedure and incision locations was provided. Postoperative outcomes and operative techniques were retrospectively evaluated. Between September 2018 and September 2020, nine laparoscopic right anterior hepatectomies using a four-incision approach were performed on patients with primary liver cancer. The surgical procedure involved several maneuvers, including blocking the Glissonean pedicle, ligation of the right hepatic artery, right branch of the portal vein and the right hepatic duct, removal of the liver parenchyma along the ischemic line and determination of the liver section based on four anatomical landmarks: the right anterior Glissonian pedicle, middle hepatic vein, root of the right hepatic vein, and retrohepatic inferior vena cava. These landmarks facilitated identification of vessels requiring ligation, resulting in reduced bleeding. The article provides clear visualization of these anatomical landmarks following right hepatectomy.
Results:
All patients in this study underwent laparoscopic anatomical right hepatectomy using a four-incision anterior approach for the treatment of liver cancer. The average duration of the operation was 354 minutes, and the estimated blood loss was 250 ml. Importantly, there were no incidents of bile leakage observed post-surgery.
Conclusion:
Proper patient positioning and precise incision placement are crucial factors for ensuring the success of the laparoscopic right anterior hepatectomy procedure. The separation of the extrahepatic Glissonean pedicle at the liver hilum to determine the hepatic resection ischemia line, as well as the identification of liver sections using four anatomical landmarks, are essential steps in the liver resection process. Additional techniques, such as adjusting the patient's posture during the operation, utilizing suction operated by the surgeon's left hand and an ultrasonic scalpel in the right hand, and suturing bleeding vessels, contribute to the safe completion of the procedure. Furthermore, maintaining a low central venous pressure (CVP) can be beneficial in reducing intraoperative bleeding.
Facilitating Test-Driven Development via Domain-Specific Languages in Computational Science Software Engineering
Authors: Gundlach Sven , Jung Reiner , Hasselbring Wilhelm
Published: December 2023

Research software plays a critical role in computational science. Therefore, the software should be of high quality. Producing such software involves efforts not only in initial development, but also during maintenance. One concept for improving the quality of software and reducing maintenance efforts is Test-Driven Development (TDD), which involves implementing tests, e.g., unit tests, as a specification for development. The effects are high test coverage and better overall software quality, but also additional effort in implementing tests. By separation of concerns, such additional efforts can be facilitated through the use of tailored notations such as domain-specific languages (DSLs). DSLs provide different levels of abstraction, for example, to separate the definition of tests from other concerns in software development. Not only does this incentivize research software engineers (RSEs) to write tests, but it also helps reduce the time required to write tests. The DSL TDD-DSL addresses this to facilitate the implementation of TDD using the Fortran Unit Testing Framework (UTF) pFUnit 4.0. TDD-DSL is focused on unit test definition and source code integration. It is implemented in Python for system under tests (SUTs) written in Fortran 90 or later. To reduce the effort of defining tests, TDD-DSL separates the test definition from the implementation and setup. Especially the latter can cause additional effort in practice, which can be mitigated by using code generators provided with the DSL tooling. The DSL allows describing test conditions as assertions and test cases as collections of assertions. TDD-DSL provides content assist within code editors for the underlying UTF, reducing the effort required in its deployment. It also supports parsing the SUT to expand the content assist with source code information and high-level error messaging for the test description. The content assist includes publicly available variables, types, functions and routines. TDD-DSL further supports the definition of unit values such as SI units to help Scientific Modelers check unit values in the software at runtime. For this purpose, the DSL tooling enables the generation of code templates for new implementations in the source code according to the defined tests. To simplify extending the DSL and improve overall maintainability, the DSL tooling employs the language server protocol (LSP) and also public available tools such as the ANTLR4 grammar. Using LSP also enables a multi-editor support to integrate the DSL tooling into different editors and IDEs. In this paper we report on the design and implementation of the DSL and its tooling as well as the evaluation with Scientific Modelers and RSEs.
Check dams overtopping scenarios from catastrophic landslides in mountains’ headwaters: case study in Kobe City, Japan
Authors: Gomez Christopher
Published: December 2023

As a Mw 8.0 Nankai Trough Earthquake is predicted with an 80% probability of occurrence within the next 30 years, the efficiency of check dams in the mountains above Kobe City is a crucial question when considering co-seismic landslide disaster risk management. In the present contribution the author aimed to define which subsection of the Sumiyoshigawa watershed may be more prone to generate impacts in downstream Kobe City from catastrophic landslides in the headwaters. For this purpose, the present state of the check dams network was analysed from the 2018 LiDAR (Light Detection and Ranging) data, taking into account the progressive infilling of the structures. As a result, the capacity of the dams in 2018 has dropped by about 1/10 of the original designed capacity, arguably because of the 2014 and 2018 heavy rainfall events. Despite of this evolution however, landslides > 20,000 m3 starting from only one of the tributary can fill all the dams and flow downstream. From this data, it is possible to prioritize dam curation programs, especially because of population ageing and shrinking is reducing both the manpower and the funds available to maintain the check dams.
HybOff: A Hybrid Offloading Approach to Improve Load Balancing in Fog Networks
Authors: Sulimani Hamza , Sulimani Rahaf , Ramezani Fahimeh , Naderpour Mohsen , Huo Huan , Jan Tony , Prasad Mukesh
Published: December 2023

Load balancing is crucial in distributed systems like fog computing, where efficiency is paramount. Offloading with different approaches is the key to balancing the load in distributed environments. Static offloading (SOS) falls short in heterogeneous networks, necessitating dynamic offloading to reduce latency in time-sensitive tasks. However, prevalent dynamic offloading (DOS) solutions often come with hidden costs that impact sensitive applications, including decision time, networks congested and distance offloading. This paper introduces the Hybrid Offloading (HybOff) algorithm, which substantially enhances load balancing and resource utilization in fog networks, addressing issues in both static and dynamic approaches while leveraging clustering theory. Its goal is to create a uncomplicated low-cost offloading approach that enhances IoT application performance by eliminating the consequences of hidden costs regardless of network size. Experimental results using the iFogSim simulation tool show that HybOff significantly reduces offloading messages, distance, and decision-offloading consequences. It improves load balancing by 97%, surpassing SOS (64%) and DOS (88%). Additionally, it increases system utilization by an average of 50% and enhances system performance 1.6 times and 1.4 times more than SOS and DOS, respectively. In summary, HybOff substantially contributes to load balancing and offloading research in fog computing.
A new method for education quality evaluation based on belief rule base with power set and evidential reasoning
Authors: Liang MinJie , Zhou Guohui , He Wei , Chen Haobing , Qian Jidong
Published: December 2023

The evaluation of education quality is an important part of the construction of the education system, and it is a direct path to improving education quality. Education quality is a complex evaluation system that takes into account a number of dimensions, such as objectives, attitudes and outcomes. In teaching practice, these dimensions are often good or bad, making it difficult to assess the results of a comprehensive evaluation, leading to the problem of ignorance. Therefore, a belief rule base with power set (PBRB) is used to construct the model for education quality evaluation. The model extends the set of evaluation results into a power set that expresses a variety of evaluation ratings that are difficult to characterize, and uses the BRB to assign belief degrees to each rating in the power set. In addition, because the evaluation of education quality requires a great number of indicators, the BRB faces the rule combination explosion problem. To solve the problem, the transformation matrix is constructed to unify the evaluation indicators, which are then fused using the evidential reasoning (ER) algorithm to reduce the number of PBRB attributes. Finally, the parameters are optimized with the projected covariance matrix adaptive evolution strategy (P-CMA-ES) to improve the model accuracy. According to the experimental results, compared with other methods, the education quality evaluation method based on the EPBRB proposed in this paper can give a clear evaluation grade and has better accuracy and stability.
The Effect of Pranayama Breathing Exercise on Fatigue in Cancer Patient Caregivers; Randomized Controlled Study
Authors: Doğan Zeynep , Güler Sevil , İnanç Mevlüde
Published: December 2023

Aim: This study was conducted with a randomized controlled experimental design to determine the effect of pranayama breathing exercise on fatigue in caregivers of cancer patients.
Methods: The study was conducted with 62 caregivers, including the Intervention (n = 31) and Control group (n = 31). Caregivers in the intervention group practiced pranayama breathing exercise for 15 to 20 minutes, once a day, three days a week for four weeks through an online interview with the researcher, while the control group did not receive any intervention. Ethics committee approval, institutional permission and permission from caregivers were obtained before commencing the study. Data were collected a questionnaire, Piper Fatigue Scale (PFS) and ECOG performance scale. Number, percentage, mean, standard deviation, chi-square and two-way repeated measures analysis of variance were utilized to evaluate the data.
Findings: It was determined that the mean PFS total score of the intervention group was 6,19±0,91 before the intervention and decreased to 4,49±0,95'e after four weeks (p > 0.05), while the PFS total score of the control group was 5,49±0,69 before the intervention and increased to 5,69±0,68 after the fourth week of the intervention (p > 0.05). Although the decrease in daily fatigue severity of caregivers in the intervention group was statistically significant (p<0.05), no statistically significant difference was detected in the fatigue severity of caregivers in the control group. In addition, it was determined that the PFS score did not differ statistically according to the groups (F=1.746; p=0.191>0.05). Moreover, the group*time interaction was detected to be statistically significant (F=112,750; p<0,001*; ES=0,653).
Conclusion: In this study, Pranayama breathing exercise was observed to reduce fatigue levels of cancer patient caregivers. According to this result, the utilization of pranayama breathing exercise can be recommended to reduce fatigue in cancer patient caregivers.
ClinicalTrials.gov ID : NCT05988320 Date: 08/04/2023
Application of Machine Learning for Prognostic Prediction in early-stage Cervical Cancer, Based on radiomics from ultrasound and MRI
Authors: Huang Xiao-wan , Ren Zhi-le , Xia wei-ting , Fu Xiao-qing , Ma Jia-yao , Powell Martin , Lin Feng , Jin Chu
Published: December 2023

Purpose: We aimed to develop a model for an early-stage cervical cancer for disease free survival (DFS) prediction using machine learning methods based on the combination of clinicopathological and radiomic features which is extracted from magnetic resonance imaging (MRI) and ultrasonography (US).
Methods: This retrospectively study included 144 patients who were randomly divided into training and testing cohort at a ratio of 6:4.Radiomic features were extracted from MRI and US images, and in total, 1180 radiomic features and 9 clinicopathological factors were obtained. Six supervised machine learning classifiers were used to assess the prediction performance based on all variables. Next, we established models based on various combinations of clinicopathological characteristic and radiomic features to get the best prediction model using LightGBM. The model’s performance was evaluated by accuracy (ACC) and area under the curve (AUC). Furthermore, unsupervised clustering analysis was performed to identify CC patient subgroups related to DFS prognosis based on the all variables.
Results: LightGBM was superior to any other classifiers in CC DFS prediction. The model that combined clinicopathological factors with radiomic features from MRI and US showed the best performance, and the corresponding values were 0.92 of ACC and 0.86 of AUC. Unsupervised clustering analysis identified a strong tendency toward the formation of two distinct groups in DFS rate among CC patients.
Conclusion: MRI and US based radiomics has the potential of DFS prediction in early-stage CC with the LightGBM classifier, and the use of predictive algorithms may facilitate the personalized treatment options.
Effect of human chorionic gonadotropin on pregnancy outcomes in patients with polycystic ovary syndrome undergoing frozen-thaw embryo transfer in hormone replacement therapy cycles: A retrospective cohort study
Authors: Dong Li , Wu Haicui , Wang Yuqi , Chen Wen , Liang Qihui , Lian Fang , Cai Pingping
Published: December 2023

Background
Endometrial preparation protocols for frozen-thawed embryo transfer (FET) and hormone replacement therapy (HRT) lack luteinizing hormone (LH) peaks and luteal function due to anovulation. Human chorionic gonadotropin (hCG) supplementation improves endometrial receptivity and promotes embryo implantation. Therefore, we aimed to compare whether there was a difference in pregnancy outcomes with hCG supplementation prior to secretory transformation in patients with polycystic ovary syndrome (PCOS).
Methods
Patients undergoing their first FET cycle between 2018 and 2022 at the university-affiliated reproductive and genetic centre participated in this retrospective cohort study. After standard screening and propensity score matching, patients were assigned to the HRT (n = 343) and HRT + hCG groups (n = 195). The primary outcomes were implantation rate (IR) and live birth rate (LBR), and the secondary outcomes included other pregnancy-related issues.
Results
There were no significant differences in IR or LBR between the two groups. However, compared to the HRT group, the HRT + hCG group had a significantly higher biochemical pregnancy rate (BPR) (75.9% vs. 64.7%, P = 0.007); the clinical pregnancy rate was also higher, but the difference was not significant (58.5% vs. 50.4%, P = 0.073). Ectopic pregnancy, ongoing pregnancy, miscarriage, and preterm birth rates were similar in both groups. After adjusting for possible confounders, logistic regression analyses showed that the effect of hCG addition on BPR (OR 0.585, 95% CI 0.393–0.872, P = 0.009) remained significant. Subsequent subgroup analyses showed a statistically significant improvement in BPR after hCG supplementation in cleavage-stage embryo transfer cycles (75.3% vs. 64.7%, P = 0.020).
Conclusions
In patients with PCOS, intramuscular hCG injection before secretory transformation had no impact on pregnancy outcomes, except for BPR. Notably, the addition of hCG did not show a significant trend towards the increased BPR observed in patients undergoing blastocyst transfer. Future prospective randomised controlled studies are required to confirm our findings.
Enhanced Adsorption of Ketoprofen and Reactive Yellow 15 from Aqueous Solutions Using Silver Nanoparticle-Modified Luffa: Characterization, Optimization, and Reusability Studies
Authors: Tavassoli Soheil , Cheraghi Setareh , Etemadifar Pardis , Mollahosseini Afsaneh , Joodaki Shirin , Sedighi Niloofar
Published: December 2023

In the current work, luffa was modified with silver nanoparticles to prepare LF/AgNPs adsorbent for the elimination of ketoprofen and reactive yellow 15 (RY15) from aqueous media. Various characterization techniques, including FTIR, XRD, and SEM-EDS analyses, were employed to confirm the successful modification of LF/AgNPs. Several key parameters such as contact time, adsorbent dosage, concentration, pH, and agitation technique were fine-tuned to optimize the adsorption process. Ketoprofen removal was found to be most effective in weakly acidic conditions (pH=5), while reactive yellow 15 adsorption was enhanced in an acidic environment (pH=2). At 298 K, the highest adsorption capacities reached 56.88 mg/g for ketoprofen and 97.76 mg/g for reactive yellow 15. The adsorption of ketoprofen followed the Temkin isotherm model, indicating a chemisorption process (R2=0.997). Reactive yellow 15, however, adhered to the Freundlich isotherm model, suggesting multilayer adsorption due to uneven distribution of active sites (R2=0.987). Additionally, the kinetics of ketoprofen adsorption were best described by the Pseudo-first order model (R2=0.989), whereas the Pseudo-second order model provided the most accurate fit for reactive yellow 15 adsorption (R2=0.997). Importantly, the LF/AgNPs adsorbent displayed consistent performance over five consecutive reuse cycles, affirming its stability and efficacy in removing both contaminants. These findings underscore the exceptional potential of LF/AgNPs as a reliable adsorbent for the removal of reactive yellow 15 and ketoprofen from aqueous solutions.
Post-myocardial infarction heart failure with reduced ejection fraction in older patients: a prospective multicentre study in Vietnam
Authors: Phan Nam Thanh Hai , Nguyen Huan Thanh , Vu Vu Hoang , Nguyen Van Tan
Published: December 2023

Background
Heart failure (HF) is a common complication of myocardial infarction (MI) in older adults. However, data on the incidence of and risk factors for post-MI HF with reduced ejection fraction (HFrEF) are limited, especially among older Asian populations. Therefore, in this study, we aimed to determine the incidence of and risk factors for post-MI HFrEF in older Vietnamese patients with MI.
Methods
This prospective multicentre study enrolled 234 patients aged ≥ 60 years hospitalised for MI without prior HF at two tertiary hospitals in Vietnam. Data on demographics, MI details, treatments, and echocardiographic left ventricular ejection fraction (LVEF) during hospitalisation were collected. Post-MI HFrEF was defined as the diagnosis of HFrEF according to European Society of Cardiology guideline criteria at follow-up 3 months post-discharge. Multivariate logistic regression analysis identified the factors associated with post-MI HFrEF.
Results
Thirty patients (12.8%) developed post-MI HFrEF. Notably, most patients (94%) underwent coronary angiography, whereas 92% underwent revascularisation during the index hospitalisation. Independent predictors were female sex (odds ratio [OR]: 4.09, 95% confidence interval [CI]: 1.43–12.89), highest troponin T levels (OR: 1.15 per 1000 ng/L increase, 95% CI: 1.03–1.30), and reduced LVEF at discharge (OR: 6.88, 95% CI: 2.37–22.06).
Conclusions
In this study, with a high rate of coronary revascularisation, the incidence of post-MI HFrEF was 12.8% at 3 months. Female sex, larger infarct size reflected by higher peak troponin T levels, and lower pre-discharge LVEF were associated with increased HFrEF risk. These findings can guide post-MI monitoring and early therapy in high-risk individuals to optimise outcomes.
Characterization of SIPs-type aquaporins and their roles in response to environmental cues in rice (Oryza sativa L.)
Authors: Miao Miao , Shi Ximiao , Zheng Xiangzi , Wu Binghua , Miao Ying
Published: December 2023

Background
Aquaporins (AQPs) facilitate water diffusion across biological membranes and are involved in all phases of growth and development. Small and basic intrinsic proteins (SIPs) belong to the fourth subfamily of the plant AQPs. Although SIPs are widely present in higher plants, reports on SIPs are limited. Rice is one of the major food crops in the world, and water use is an important factor affecting rice growth and development; therefore, this study aimed to provide information relevant to the function and environmental response of the rice SIP gene family.
Results
The rice (Oryza sativa L. japonica) genome encodes two SIP-like genes, OsSIP1 and OsSIP2, whose products are predominantly located in the endoplasmic reticulum (ER) membrane. Heterologous expression in a yeast aquaglyceroporin-mutant fps1Δ showed that both OsSIP1 and OsSIP2 made the cell more sensitive to KCl, sorbitol and H2O2, indicating facilitated permeation of water and hydrogen peroxide. In addition, the yeast cells expressing OsSIP2 were unable to efflux the toxic methylamine taken up by the endogenous MEP permeases, but OsSIP1 showed subtle permeability to methylamine, suggesting that OsSIP1 may have a wider conducting pore than OsSIP2. Expression profiling in different rice tissues or organs revealed that OsSIP1 was expressed in all tissues tested, whereas OsSIP2 was preferentially expressed in anthers and weakly expressed in other tissues. Consistent with this, histochemical staining of tissues expressing the promoter-β-glucuronidase fusion genes revealed their tissue-specific expression profile. In rice seedlings, both OsSIPs were more or less up-regulated under different stress conditions, including osmotic shock, high salinity, unfavorable temperature, redox challenge and pathogen attack, as well as by hormonal treatments such as GA, ABA, MeJA, SA. However, a reduced expression of both OsSIPs was observed under dehydration treatment.
Conclusions
OsSIP1 and OsSIP2 form the two-member Small Intrinsic Proteins subfamily of aquaporins in rice, they conduct water and H2O2. Moreover, OsSIP1 has a weak ability to conduct methylamine while OsSIP2 does not. Both proteins are mainly localized to the ER, although transient localization to the plasma membrane is not excluded. OsSIP1 is widely expressed in most tissues and during developmental stages, while OsSIP2 expressed preferentially in the anther. Both genes are up-regulated under a variety of stress conditions and by different hormone treatments, except that dehydration moderately reduces their expression. Our results pave the way for further deciphering the unique function of SIP-like aquaporins in substrate transport, growth and development, and environmental response in rice.
Post-ligation cardiac syndrome after surgical versus transcatheter closure of patent ductus arteriosus in low body weight premature infants: a multicenter retrospective cohort study
Authors: Duboue Pierre-Marie , Padovani Paul , Bouteiller Xavier Paul , Martin-Kabore Frédérique , Benbrik Nadir , Gronier Céline Grunenwald , Bouissou Antoine , Garnier Elodie , Mitanchez Delphine , Flamant Cyril , Rozé Jean-Christophe , Baruteau Alban-Elouen , Lefort Bruno
Published: December 2023

Purpose
Transcatheter patent ductus arteriosus (PDA) closure is a safe and effective alternative to surgical ligation in low-body-weight infants. Post-ligation cardiac syndrome (PLCS) is defined as severe hemodynamic and respiratory collapse within 24 hours of PDA closure, requiring initiation or an increase of an inotropic agent by > 20% of preligation dosing and an absolute increase of at least 20% in ventilation parameters compared with the preoperative value. Whilst PLCS is routinely observed after surgery, its incidence remains poorly described following transcatheter closure. This study aimed to compare the incidence of PLCS after surgical versus transcatheter closure of PDA in low-body-weight premature infants.
Methods
Propensity scores were used to compare surgical (N = 78) and transcatheter (N = 76) groups of preterm infants who underwent PDA closure at a procedural weight less than 2000 grams in two tertiary institutions between 2009 and 2021. The primary outcome was the incidence of PLCS. Secondary outcomes included overall mortality before discharge, risk factors for PLCS, and post-procedural complications.
Results
Procedural success was 100% in both groups. After matching, transcatheter group experienced no PLCS vs 15% in the surgical group (p = 0.012). Furthermore, overall mortality (2% vs 17%; p = 0.03) and major complications (2% vs 23%; p = 0.002) were higher in the surgical group. Surgery (100% vs 47%; p < 0.01), gestation age (25 ± 1 vs 26 ± 2 weeks, p < 0.05) and inotropic support before closure (90% vs 29%; p < 0.001) were associated with PLCS occurrence.
Conclusions
Transcatheter PDA closure may be equally effective but safer than surgical PDA closure in low-body-weight premature infants.
Integrating perspectives of transgender and gender-diverse youth, parents, and professionals to support their health and wellbeing – a mixed-method study protocol
Authors: Servais Julie , Vanhoutte Bram , Aguirre-Sánchez-Beato Sara , Aujoulat Isabelle , Kraus Cynthia , T’Sjoen Guy , Tricas-Sauras Sandra , Godin Isabelle
Published: December 2023

Background
The current literature highlights a strong link between the poor health outcomes of transgender and gender diverse (TGD) individuals and their negative experiences in various areas of life. Most of these publications rely on adults' memories, lacking a focus on the current experiences and needs of young transgender and gender-diverse individuals. Furthermore, previous studies on support for these young people often solely consider the perspectives of TGD adults or professionals and rarely involve parents' viewpoints.
Methods
This study will use a mixed sequential method with a participatory approach. Firstly, the qualitative phase will explore the difficulties and needs of TGD (15–20 years old) and of the families and professionals who support them. Results from this part will be used to develop the questionnaire for the quantitative phase, with the help of a community board. Secondly, based on participatory epidemiological research, the quantitative phase will use an intersectional perspective to measure the impact of individual and structural factors on the quality of life and well-being of transgender and gender-diverse young people. Finally, a co-creation phase will be undertaken to formulate recommendations based on the results of the first two phases.
Discussion
This research aims at better understanding the influence of gender identity on the quality of life and health of TGD young people and their families and to identify protective and risk factors that affect their vulnerabilities.
Ethics and dissemination
This study has been approved by the Ethics Committee of the Erasme Faculty Hospital (CCB B4062023000140). As this research is participatory and part of a PhD dissertation, we aim to disseminate the results through our partners' networks and structures locally, and internationally through conferences and peer-reviewed journals.
A deep learning radiomics model based on CT images for predicting the biological activity grading of hepatic cystic echinococcosis
Authors: Damola Maihemitijiang , Yang Jing , Abulaiti Adilijiang , Mutailifu Aibibulajiang , Aihait Diliaremu , Abulizi Abudoukeyoumujiang , tuerxun Kahaer , Zou Xiaoguang , Nijiati Mayidili
Published: December 2023

Objective This work aims to explore the potential applications of a deep learning radiomics (DLR) model, which is based on CT images, in predicting the biological activity grading of hepatic cystic echinococcosis.
Methods A retrospective analysis of 160 patients with hepatic echinococcosis (109 cases of CE1 and 51 cases of CE2) was performed. A training set of 127 cases and a validation set of 33 cases were randomly divided from the data. Volume of interests (VOIs) were drawn from each patient’s CT image, and radiomics features and deep neural network features were extracted. Feature selection was performed on the training set, and radiomics score (Rad Score) and deep learning score (Deep Score) were calculated. Seven diagnostics models (based on logistic regression algorithm) for the biological activity grading were constructed using the selected radiomics features and two deep model features respectively. All models were evaluated using the receiver operating characteristic curve, and the area under the curve (AUC) was calculated. A nomogram was constructed using the combined model, and its calibration, discriminatory ability, and clinical utility were assessed.
Results A total of 12 optimal features were selected from the radiomics features, and 6 and 10 optimal features were selected from two deep learning network (DLN) features (3D-ResNet-34, 3D-ResNet-50), respectively. For biological activity grading of hepatic cystic echinococcosis, the combined model demonstrated strong diagnostic performance, with an AUC value of 0.888 (95%CI:0.837-0.936) in the training set and 0.876 (0.761-0.964) in the validation set. The clinical decision analysis curve indicated that the DLR model had a greater clinical benefit than the single radiomics model and deep feature model, while the calibration curve revealed that the nomogram's prediction result was highly compatible with the actual result.
Conclusion The DLR model can be used for predicting the biological activity grading of hepatic echinococcosis.
Neuroprotective effects of Quinpirole in lithium chloride pilocarpine-induced epilepsy in rats and its underlying mechanisms
Authors: Wang Hui , Zhao Yongheng , Zhang Dongqing , Li Jun , Yang Kun , Yang Junli , Li Baomin
Published: December 2023

Epilepsy is a common neurological disorder that presents with challenging mechanisms and treatment strategies. This study investigated the neuroprotective effects of Quinpirole in lithium chloride pilocarpine-induced epileptic rats and explored its potential mechanisms.
Methods:
Lithium chloride pilocarpine was used to induce an epileptic model in rats, and the effects of Quinpirole on seizure symptoms and cognitive function were evaluated. The Racine scoring method, electroencephalography, and Morris water maze test were used to assess the severity of the seizures as well as learning and memory function in the group of rats with epilepsy. Additionally, immunohistochemistry and Western blot techniques were used to analyze the expression levels and morphological changes in glutamate receptor 2 (GluR2; GRIA2), BAX, and BCL2 proteins in the hippocampi of the group of rats with epilepsy.
Results:
First, it was confirmed that the symptoms in the group of rats with epilepsy were consistent with features of epilepsy. Furthermore, the group of rats with epilepsy demonstrated decreased learning and memory function in the Morris water maze test. Additionally, gene and protein levels of GluR2 in the hippocampi of the group of rats with epilepsy were significantly reduced.
Treatment with Quinpirole significantly delayed seizure onset and decreased the mortality rate after the induction of a seizure. Furthermore, electroencephalography showed a significant decrease in the frequency of the spike waves. In the Morris water maze test, rats from the Quinpirole treatment group demonstrated a shorter latency period to reach the platform and an increased number of crossings through the target quadrant. Network pharmacology analysis revealed a close association between Quinpirole and GluR2 as well as its involvement in the cAMP signaling pathway, cocaine addiction, and dopaminergic synapses.
Furthermore, immunohistochemistry and Western blot analysis showed that Quinpirole treatment resulted in a denser arrangement and a more regular morphology of the granule cells in the hippocampi of the group of rats with epilepsy. Additionally, Quinpirole treatment decreased the expression of BAX protein and increased that of BCL2 protein.
Conclusion:
The current study demonstrated that Quinpirole had neuroprotective effects in the epileptic rat model induced by lithium chloride pilocarpine. Further, it improved the symptoms of seizures as well as the learning and memory function of the rats and was associated with the modulation of the expression of GluR2, BAX, and BCL2 proteins. These findings provided clues that would be important for further investigation of the therapeutic potential of Quinpirole and its underlying mechanisms for epilepsy treatment.
Microvesicles derived from mesenchymal stem cells inhibit ARDS pulmonary fibrosis partly through HGF
Authors: Chen Qi-Hong , Zhang Ying , Gu Xue , Yang Penglei , Yuan Jun , Yu Lina , Chen Jianmei
Published: December 2023

Objective
We investigated the effects and mechanisms of mesenchymal stem cell (MSC-MVs) derived microvesicles on pulmonary fibrosis in acute respiratory distress syndrome (ARDS) mice models.
Methods
The MSC-MVs with low hepatocyte growth factor (HGF) expressions (siHGF-MSC-MVs) were obtained by Lentivirus transfection. The ARDS pulmonary fibrosis mice models were established by injecting them with lipopolysaccharide (LPS) three times, after which MSC-MVs or siHGF-MSC-MVs were injected via the tail veins of ARDS pulmonary fibrosis mice. Homing of MSC-MVs in lung tissues were investigated by near-infrared live imaging. Immunohistochemical, Western blot, enzyme linked immunosorbent assay (ELISA) and other methods were used to detect the expressions of pulmonary fibrosis-related proteins, and to compare the effects of MSC-MVs on pulmonary fibrosis and fibrosis-related indicators in ARDS pulmonary fibrosis mice models.
Results
After tail vein injection, the MSC-MVs gradually migrated and homed to damaged lung tissues in ARDS mice models. Treatment of pulmonary fibrosis mice with MSC-MVs significantly reduced the lung injury and pulmonary fibrosis scores. However, low expressions of HGF (siHGF-MSC-MVs) significantly inhibited the roles of MSC-MVs (p < 0.05). Compared with the ARDS pulmonary fibrosis group, the MSC-MVs group exhibited suppressed expressions of type I collagen antigen, type III collagen antigen, TGF-β and α-SMA proteins while the siHGF-MVs group exhibited significantly elevated expressions of the proteins. In addition, compared to the pulmonary fibrosis group, pulmonary compliance (Cydn) and PO2/FiO2 were significantly low in the MSC-MVs group, and the effects of MSC-MVs were significantly inhibited after low expressions of HGF (all p < 0.05).
Conclusion
The MSC-MVs inhibited pulmonary fibrosis in ARDS mice partly via HGF mRNA transfer.
Use of mobile fitness app to improve pelvic floor muscle training in puerperal women with gestational diabetes mellitus: a randomized controlled trial
Authors: He Xiaocheng , Xie Yaping , Xie Baoyuan , Zhao Meijing , Zhang Honghui , Zhao Xiaoshan , Zhao Huifen
Published: December 2023

Background
Gestational diabetes mellitus (GDM) is a risk factor for postpartum urinary incontinence. Pelvic floor muscle training (PFMT) improves pelvic floor dysfunction in puerperal women, but patient compliance is low. Mobile Health(mHealth)is a promising solution.
Objective
To investigate PFMT compliance and effects on pelvic floor muscles in GDM puerperal women guided by the mobile fitness app KEEP.
Methods
This randomized controlled trial included puerperal women with GDM (n = 72) who were delivered at a tertiary general hospital, selected from November 2021 to April 2022 using convenience sampling, and randomly divided into control (n = 36) and experimental (n = 36) groups. The control group performed PFMT based on routine postpartum PFMT training instruction. The experimental group performed PFMT based on KEEP. Both groups had a 4-week intervention period. The PFMT compliance, International Consultation on Incontinence Questionnaire Short Form (ICIQ-SF), Pelvic Muscle Self-efficacy Scale, and the Knowledge, Attitude, Belief, and Practice (KAP) scores of PFMT in puerperal women in the groups were compared pre- and post-intervention. Pelvic floor surface electromyographic biofeedback was used to compare the post-intervention pelvic floor muscle strength between the two groups.
Results
Compared with the control group, the test group had higher post-intervention maternal PFMT compliance, pelvic floor muscle strength, pelvic floor muscle self-efficacy, and KAP scores (p < 0.05); incontinence scores were lower (p < 0.05). Pelvic floor muscles in both groups recovered better post-intervention (p < 0.05).
Conclusion
The KEEP app can improve PFMT adherence, urinary incontinence, KAP scores, self-efficacy, and pelvic floor muscle strength in GDM puerperal women and promote pelvic floor rehabilitation after delivery.
A Multi-objective Grey Wolf Optimizer for Power Scheduling Problem in Smart Home Using Renewable Energy Systems
Authors: Makhadmeh Sharif Naser , Al-Betar Mohammed Azmi , Al-Obeidat Feras , Alomari Osama Ahmad , Abasi Ammar Kamal , Tubishat Mohammad , Elgamal Zenab , Alomoush Waleed
Published: December 2023

This paper presents the energy planning problem (EPP) as an optimization problem to find the optimal schedules to minimize energy consumption costs and demand and enhance users' comfort levels. The grey wolf optimizer (GWO), One of the most powerful optimization methods, is adjusted and adapted to address EPP optimally and achieve its objectives efficiently. The GWO is adapted due to its high performance in addressing NP-complex hard problems like the EPP, where it contains efficient and dynamic parameters that enhance its exploration and exploitation capabilities, particularly for large search spaces. In addition, new power and real-world resources based on solar renewable energy systems (RESs) are combined with the proposed GWO to enhance its performance and ensure optimizing EPP objectives. Furthermore, EPP is presented as a multi-objective planning problem to optimize all objectives simultaneously. To efficiently investigate the proposed method performance, the results obtained by the GWO with the RESs are compared in three stages: comparison with original methods without RESs, comparison with methods using RESs, and comparison with state-of-the-art. The obtained results proved the robust performance of the proposed method in handling EPP and optimizing its objectives.
AutoDetect: Novel Autoencoding Architecture for Counterfeit IC Detection
Authors: Bhure Chaitanya , Nicholas Geraldine Shirley , Ghosh Shajib , Asadi Navid , Saqib Fareena
Published: December 2023

The global electronic supply chain industry is a complex and interconnected network of companies, organizations, and individuals that collaborate to produce and distribute electronic devices and components. It plays a critical role in the global economy, as electronic devices have become an essential part of modern life. Electronic components and devices are produced and distributed across the world, with different regions and countries specializing in different aspects of the supply chain. This global nature of the electronic supply chain industry also poses challenges, particularly in terms of quality control and supply chain transparency involving counterfeit components owing to its interconnected nature. Counterfeiting of integrated circuits (ICs) and semiconductor devices is a significant challenge that poses threats to the safety and reliability of electronic devices, the global economy, intellectual property rights, and the overall sustainability of the electronic supply chain industry. To address this challenge, we propose a novel autoencoding architecture for counterfeit IC detection using a labeled database. The proposed architecture achieves an overall accuracy of 83% which is approximately ≈ 20% more than the existing transfer learning approaches.
Study of research hotspots and development trends of image security under the background of deep learning
Authors: Guo Yuan , Liu Ziqi
Published: December 2023

As an important carrier of information transmission, images are widely used in human-computer interaction, digital twins, cloud computing, and even people’s daily life. Image security is directly related to personal privacy, business secrets and national security. It is of great significance to strengthen the research of image security. Deep learning is a machine learning method based on neural networks, which enables the processing and analysis of complex data by simulating the way human brain neurons work and provides strong support for research in the field of image security. With the rapid development of deep learning technology, its application in the field of image processing and security is increasingly extensive. To gain a deeper understanding of the current situation and cutting-edge trends of deep learning-based image security research. This paper uses CiteSpace, a bibliometric tool, to analyze the 2009-2022 research results on "image security" and "deep learning" in the core database of Web of Science. Accurately grasp the stage of development and characteristics of image security research in the world through data analysis. It is found that the research hotspots mainly cover image encryption, image steganography, image watermarking, and other technical applications. In the past decade, great progress has been made in the field of image security. The research on techniques such as synthetic image identification, image tampering detection, and adversarial samples is rapidly emerging. Future research on image security deep learning-based will focus on the directions of anti-attacks, robustness enhancement, privacy protection, and data sharing. We hope this study will provide new research ideas and thinking perspectives to draw on for the future direction of image security.
Trend patterns of nitrogen dioxide: OMI measurements and Machine Learning to understand the global anthropogenic emissions
Authors: Murillo-Tovar Mario Alfonso , Herrera-López Enrique Jaime , Saldarriaga-Noreña Hugo Albeiro , Díaz-Torres José de Jesús
Published: December 2023

Tropospheric dioxide nitrogen is one of the criteria pollutants considered a toxic gas that contributes to climate change and affects public health. Anthropogenic activities are the primary NO2 sources affecting the planetary ecosystems. The Ozone Monitoring Instrument (OMI) on board AURA Missions is one of the most robust projects contributing to the NO2 investigation. A methodological coupling based on spatial analysis, clustering, machine learning, and statistical validation helped to analyze the OMI satellite data and its interactions with socioeconomic factors. Spatial contrasts show differences between continental and marine domains, highlighting the influence of coastal urban centers on the near marine areas; differences between hemispheres and latitudinal changes overall in the Pacific and Atlantic oceans; contrasts between urban and rural areas in continents; and outstanding regions by their high NO2 emissions. The trend analysis outlined regional contrasts that contribute to understanding the impact of economic activities and environmental policy implementation. From 2005 to 2021, trend patterns characterization established the framework to correlate the population size and the GDP of more than 250 developed urban centers worldwide. Prominent maximum NO2 densities between 2011 and 2013 stand out among the four trend patterns, outlining a point inflection (peak component) in the trend direction of several regions. The correlation, including all cities, followed a significant moderate relationship (R=0.573, p~0.000) where the population explained 33.7% of the productivity. However, the correlations by subgroups considering trend pattern classification indicated significant moderate to strong relationships for almost all trend types (R from 0.689 to 0.814, p~0.000), where the population explains 47.5 to 66.2% of the productivity. These results partially show the direct cause-effect relationship between the high NO2 emissions and development levels in urban centers. Conversely, the wide scattering in such correlations suggests the gradual and positive effects of Environmental policies in favor of better air quality, different from the sudden decrease in NO2 densities caused by confinement and preventive measures against COVID-19.
A Multi-layer Graph Neural Network Framework for Cold Start Recommendation
Authors: Whitman Ignacio , Havisham Booker , Hurst Greta , Serrano Ximena
Published: December 2023

Traditional recommendation algorithms often face challenges such as data sparsity or cold start issues and may not effectively explore the correlation between rating information and auxiliary data. Convolutional Graph Neural Networks are broadly divided into two mainstream categories: spectral-domain-based convolution methods and spatial-domain-based convolution methods. In 2013, Bruna et al. published the first study on graph neural networks based on spectral-domain convolution. This study defined a specific way of graph convolution based on spectral graph theory. Since then, spectral-domain-based convolutional graph neural networks have received increasing attention . A series of spectral methods based on graph Fourier transform have been proposed. In these methods, spectral convolutional neural networks assume that the filters in the Fourier transform are a set of trainable parameters, laying the theoretical foundation for subsequent research. Following this trend, Kipf et al. proposed the groundbreaking Graph Convolutional Network (GCN), which efficiently approximates neighborhood convolution and aggregation by using Chebyshev polynomials to achieve a first-order approximation. The aim of this paper is to address the cold start problem using graph neural network technology while ensuring that the model retains the ability to leverage node content features. Additionally, the goal is to enhance the recommendation performance of the model on this basis, aiming to achieve or surpass the effectiveness of existing methods.