Miracle-dz/UniFAR-FTUs huggingface dataset:
Pharmacological Probes to Validate Biomarkers for Analgesic Drug Development

There is an urgent need for analgesics with improved efficacy, especially in neuropathic and other chronic pain conditions. Unfortunately, in recent decades, many candidate analgesics have failed in clinical phase II or III trials despite promising preclinical results. Translational assessment tools to verify engagement of pharmacological targets and actions on compartments of the nociceptive system are missing in both rodents and humans. Through the Innovative Medicines Initiative of the European Union and EFPIA, a consortium of researchers from academia and the pharmaceutical industry was established to identify and validate a set of functional biomarkers to assess drug-induced effects on nociceptive processing at peripheral, spinal and supraspinal levels using electrophysiological and functional neuroimaging techniques. Here, we report the results of a systematic literature search for pharmacological probes that allow for validation of these biomarkers. Of 26 candidate substances, only 7 met the inclusion criteria: evidence for nociceptive system modulation, tolerability, availability in oral form for human use and absence of active metabolites. Based on pharmacokinetic characteristics, three were selected for a set of crossover studies in rodents and healthy humans. All currently available probes act on more than one compartment of the nociceptive system. Once validated, biomarkers of nociceptive signal processing, combined with a pharmacometric modelling, will enable a more rational approach to selecting dose ranges and verifying target engagement. Combined with advances in classification of chronic pain conditions, these biomarkers are expected to accelerate analgesic drug development.
Covid-19 Pandemic and Open Economy: Can it Influence the Stability of the Financial System in Sigeract Countries?

This study discusses the impact of Covid -19 and an open economy on financial system stability in the Six Global Foreign Exchange Rate Countriefree, thesee countries are Brazil, Mexico, Colombia, Russia, Argentina,and Indonesia. This study uses the simultaneous regression analysis method (2TLS) with testing using Eviews 10 and the Covid-19 impact differential test using SPSS. The results of simultaneous regression analysis show that exports and Inflation have a positive inelastic effect on financial system stability. In addition,direct investment and interest rates have a positive lasting impactt on financial system stability. The different assets show that there is no significant difference in Inflation in Brazil and Mexico, but interest rates have significant differences before and after covid-1; in Colombia, Russia, Argentina,and Indonesia, there are substantial differences in Inflation and interest rates before and after covid-19.
Peri-Implant Marginal Bone Changes around Dental Implants with Platform-Switched and Platform-Matched Abutments: A Retrospective 5-Year Radiographic Evaluation

Preserving the marginal bone level (MBL) is essential for the long-term success of dental implant therapy, and bone remodeling around dental implants is considered to vary with time. Numerous studies comparing the platform-switching (PS) and platform-matching (PM) dental implants have indicated that PS dental implants showed a lesser reduction for the MBL, and the majority of them had a relatively short period. This study aimed to evaluate vertical and horizontal bone defects by using digital periapical radiographs to examine the changes in MBL around PM and PS dental implants over 5 years after functional loading. The vertical MBL (vMBL) was measured from the implant–abutment junction to the first bone-to-implant contact. The horizontal MBL (hMBL) was measured from the implant–abutment junction to the bone crest. All data were presented as means ± standard errors. Paired and independent t-tests with Welch’s correction were used to analyze the data. A total of 61 dental implants in 38 patients after 5 years of functional loading were evaluated. Over time, PS dental implants were more likely to gain bone; by contrast, PM dental implants were more likely to lose bone during the observation time. Changes in vMBL for PS dental implants were significantly less than those for PM dental implants at 1-year (p = 0.045), 3-year (p = 0.021), and 5-year (p = 0.010) loading. Likewise, changes in hMBL for PS dental implants were significantly smaller than in those for PM dental implants at 3-year (p = 0.021) and 5-year (p = 0.006) loading; however, the changes were minimal in both approaches. PS dental implants had a significant increment in the percentage of bone integration, whereas that for PM dental implants dropped over time, with no significance. In PS dental implants that occlude with natural teeth, vertical and horizontal bone gain was observed, and it was significant at 3 years (p = 0.023). A significant horizontal bone gain was observed in the opposing natural teeth at 3-year (p = 0.002) and 5-year loading (p = 0.002). The PS concept appears to preserve more MBL around dental implants by stabilizing the vMBL and hMBL over a 5-year period. A minimal marginal bone change was detected in both concepts. The opposing natural teeth at PS dental implants showed a favorable effect on marginal bone tissues.
A Study on a Complex Flame and Smoke Detection Method Using Computer Vision Detection and Convolutional Neural Network

This study sought an effective detection method not only for flame but also for the smoke generated in the event of a fire. To this end, the flame region was pre-processed using the color conversion and corner detection method, and the smoke region could be detected using the dark channel prior and optical flow. This eliminates unnecessary background regions and allows selection of fire-related regions. Where there was a pre-processed region of interest, inference was conducted using a deep-learning-based convolutional neural network (CNN) to accurately determine whether it was a flame or smoke. Through this approach, the detection accuracy is improved by 5.5% for flame and 6% for smoke compared to when a fire is detected through the object detection model without separate pre-processing.
The Asymptotic Giant Branch

For stars with masses between about 1 and 8 M ⊙ the ascent of the Asymptotic Giant Branch (AGB) is the last nuclear powered evolutionary stage. Following this the stars eject a planetary nebula and fade as white dwarfs. Although only short in duration, the AGB is very important due to the nucleosynthesis which occurs. Here we briefly review the species produced and the mechanism for their production. We then discuss the current uncertainties in quantitative predictions of this nucleosynthesis, which centre on our determination of convective boundaries.
Impact of academic accreditation on teaching and learning: faculty members’ perceptions

ABSTRACT This mixed-method research study investigates the impact of programme accreditation conducted by the Saudi National Commission for Accreditation and Assessment (NCAAA) on teaching and learning at the Imam Abdulrahman Bin Faisal University in the Kingdom of Saudi Arabia. The quantitative data was collected through an online survey consisting of 53 statements as indicators of the teaching and learning practices required by the NCAAA accreditation and ten flow-up qualitative interviews. Furthermore, this study draws on the findings of numerous contemporary studies worldwide to discern the global patterns and compare these with our findings. The results revealed varying degrees of improvement in all practices. The highest impact was on learning outcomes practices. The findings indicate the importance of designing quality assurance policies close to academic realities to drive improvement, which the NCAAA sought to achieve through a learning outcomes-led approach and a gradual implementation intended to promote a quality culture to reform an educational system in need of transformation.
Renal Toxicities in Cancer Patients Receiving Immune-Checkpoint Inhibitors: A Meta-Analysis

Aim: We performed a meta-analysis of the available clinical trials of immune-checkpoint inhibitors to assess risk differences and relative risks of renal toxicity. Methods: 17 randomized phase III studies were selected, including 10,252 patients. Results: The administration of immune-checkpoint inhibitors resulted in an overall low-grade, high-grade and all-grade renal toxicity Risk Difference of: 0.746% (95% CI 0.629% to 1.15%, p < 0.001—random), 0.61% (95% CI, 0.292–0.929%, p < 0.001—fixed) and 1.2% (95% CI, 0.601–1.85%—random), respectively. The pooled Relative Risk of low-grade, high-grade and all-grade renal toxicity was: 2.185 (95% CI 1.515–3.152—fixed), 2.610 (95% CI, 1.409–4.833, p = 0.002—fixed) and 2.473 (95% CI, 1.782–3.431, p < 0.001—fixed), respectively. An increased risk of renal toxicity was evident in some subgroups more than others. Conclusion: Immune-checkpoint inhibitors are associated with an increased risk of renal toxicity.
Analysis of Risk Factors for Anastomotic Leakage After Laparoscopic Anterior Resection of Rectal Cancer and Construction of a Nomogram Prediction Model

Objective To explore the risk factors of anastomotic leakage (AL) after laparoscopic anterior resection (AR) of rectal cancer and establish a nomogram prediction model. Methods Clinical and surgical data of patients who underwent AR of rectal cancer at Sichuan Cancer Hospital from January 2017 to December 2020 were retrospectively collected. Univariate and multivariate logistic regression analyses were used to screen the independent risk factors of AL after AR. A nomogram risk prediction model was established based on the selected independent risk factors and the predictive performance of nomogram was evaluated. Results A 1013 patients undergoing laparoscopic AR were included, of which 67 had AL, with an incidence of 6.6%. Univariate and multivariate logistic regression analyses showed that male gender, tumors distance from the anus verge of ≤ 5cm, tumors distance from the anus verge of 5–10cm, circumferential tumor growth, operation time ≥ 240min, and no diverting stoma were independent risk factors for AL after AR. A nomogram prediction model was established based on these results. The calibration curve showed that the predicted occurrence probability of the nomogram model was in good agreement with the actual occurrence probability. The area under the receiver operating characteristic (ROC) curve was 0.749. Conclusion The nomogram prediction model based on the independent risk factors of patients undergoing AL after AR can effectively predict the probability of AL.
Autonomous Learning Capacity of University Students’ during the COVID-19 Pandemic

The research aimed to describe the students’ autonomous learning capacity during the COVID-19 pandemic. Descriptive quantitative research was conducted with 269 students of Education faculty, the University of Pahlawan as the research sample that was selected through a simple random sampling technique. The questionnaire was used as a research instrument. The questionnaire with four Likert scales was distributed to the sample with five indicators. To analyze the data, the researcher used descriptive statistical analysis. The findings of the research were as follows: 1). The students agreed that they could formulate their own learning objectives. In addition, several learning activities designed are prepared to help them to reach the learning objective. 2). They agreed that they adjusted the study plan by considering personal needs, including the time schedule as detailed as possible. 3). They claimed that they are acknowledged with learning strategy and implement it in the routine learning process intentionally. 4). The students reported that several changes were adapted in terms of learning strategy if it is necessary as well as considering personal capability. 5). They agreed that monitoring and evaluation of the whole learning process are necessary. This study concluded that the integration of technology in the learning process during the COVID-19 pandemic is a positive thing.
Influence of Menstrual Cycle on Leukocyte Response Following Exercise-Induced Muscle Damage

We investigated the influence of the menstrual cycle (MC) on leukocyte response after exercise-induced muscle damage (EIMD). During the early follicular (E-FP, n = 12) or mid-luteal phase (M-LP, n = 12), 24 untrained females with eumenorrhea performed 60 eccentric exercises using nondominant arms. Blood samples were collected at pre- and 4, 48, and 96 h postexercise to analyze estradiol and progesterone concentrations, leukocyte count and fractionation, and creatine kinase (CK) activity. We also assessed the maximal voluntary isometric contraction torque of elbow flexion, range of motion in the elbow joint, upper-arm circumference, and muscle soreness as indirect muscle damage markers at pre-; immediately post-; and 4, 48, and 96 h postexercise. The percent change in neutrophil counts from pre- to 4 h postexercise was lower in M-LP than in E-FP (E-FP, 30.7% [15.9–65.7%] vs. M-LP, 10.3% [−2.3–30.0%]; median [interquartile range: 25–75%]; p = 0.068). Progesterone concentration at pre-exercise was significantly negatively correlated with the percent change in neutrophil counts from pre- to 4 h postexercise in M-LP (r = −0.650, p = 0.022). MC did not affect CK activity or other muscle damage markers. Thus, progesterone concentration rather than MC may be related to neutrophil response following EIMD.
Pre-Hospital Delay in Acute Ischemic Stroke Care: Current Findings and Future Perspectives in a Tertiary Stroke Center from Romania—A Cross-Sectional Study

Background and objectives: The time interval between stroke onset and hospital arrival is a major barrier for reperfusion therapies in acute ischemic stroke and usually accounts for most of the onset-to-treatment delay. The present study aimed to analyze the pre-hospital delays for patients with acute ischemic stroke admitted to a tertiary stroke center in Romania and to identify the factors associated with a late hospital arrival. Material and methods: The study population consisted of 770 patients hospitalized with the diagnosis of acute ischemic stroke in the University Emergency Hospital Bucharest during a 6-month period, between 1 January and 30 June 2018. Data regarding pre-hospital delays were prospectively collected and analyzed together with the demographic and clinical characteristics of the patients. Results: In total, 31.6% of patients arrived at the hospital within 4.5 h from stroke onset and 4.4% in time intervals between 4.5 and 6 h from the onset, and 28.7% of the patients reached the hospital more than 24 h after onset of symptoms. Transport to hospital by own means was the only factor positively associated with arrival to hospital > 4.5 h from stroke onset and more than doubled the odds of late arrival. Factors negatively associated with hospital arrival > 4.5 h after stroke onset were prior diagnosis of atrial fibrillation, initial National Institute of Health Stroke Scale (NIHSS) score ≥ 16 points, presence of hemianopsia, facial palsy and sensory disturbance. Factors increasing the odds of hospital arrival after 24 h from stroke onset were living alone and living in rural areas. Conclusions: Almost one in three ischemic stroke patients presenting to our center reaches hospital more than 24 h after onset of symptoms. These findings highlight the need for urgent measures to improve not only stroke awareness but also pre-hospital protocols in order to provide timely and appropriate care for our stroke patients.
Sparsity-Inducing Methods in Imaging Sciences and Partial Differential Equations

Sparsity has played a central role in many fields of applied mathematics such as signal processing, image processing, compressed sensing, and optimization. Theoretically, sparse solutions are of interested in these fields because they can be recovered exactly from ill-posed inverse problems. Numerically, methods for computing sparse solutions have fast and efficient implementations, making them extremely practical. In terms of modeling, sparsity is promoted through the addition of an $L^1$ norm (or related quantity) as a constraint or penalty in a variational model. This methodology is also related to various properties of solutions of partial differential equations (PDEs) including compact support sets for free boundary problems and sparse representation of the solution space. Recently, sparsity-inducing methods used in image processing and compressed sensing have been applied to computational PDEs and applied harmonic analysis. Part I of the thesis focuses on the construction of efficient numerical schemes for PDEs with sparse structures. From the early theoretical work on variational inequalities, it was showed that PDEs with $L^1$ subdifferential terms have unique solutions with compact supports which can be considered to be sparse in the discrete sense. We considered an elliptic PDE derived from a variational principle or a parabolic PDE associated with the gradient flow of a convex functional. In order to compute the solutions, fast computational schemes were introduced to solve the resulting minimization problem. Those methods can handle the multivalued nature of the sub-gradient $\partial \|u\|_{L^1}$, which involve the proximal operator of the $L^1$ term. Although these methods are popular in imaging and data science, their applications in PDEs are limited.The $L^1$-based methodology was also introduced to elliptic obstacle problems and related free boundary problems such as Hele-Shaw flow, two-phase membrane, and divisible sandpile. Our numerical methods are based on a reformulation of those PDEs in terms of $L^1$-like penalties on the associated variational problems. One advantage of the proposed methods is that the free boundary inherent in the obstacle problem arises naturally in the energy minimization without any need for problem specific or complicated discretization. Moreover, the numerical solution can be computed using fast and simple algorithms.Part II of the thesis focuses on data decomposition methods to extract important features or recover the intrinsic properties from the original data. From the variational approaches, a unifying retinex framework was developed to decompose an image into two components with certain sparsity and fidelity priors. The unified formulation connects many retinex implementations into one model. Moreover, new retinex applications were introduced within a single framework which includes shadow detection, cartoon-texture decomposition, nonuniform illumination correction and color contrast enhancement.
Electricity Consumption Optimization Using Thermal and Battery Energy Storage Systems in Buildings

Energy storage system (ESS) plays a key role in peak load shaving to minimize power consumption of buildings in peak hours. This paper proposes a novel energy management unit (EMU) to define an optimal operation schedule of ESSs by employing metaheuristic and mathematical optimization approaches. The proposed EMU uses a thermal energy storage system (TESS) and a battery energy storage system (BESS) to store the energy in off-peak periods and discharge it in high load demands. We formulate the charging/discharging schedule of TESS and BESS as an optimization problem. Then, particle swarm optimization (PSO) is employed to obtain the optimal schedule due to its computational time efficiency. The mathematical approach is also applied to prove the convexity of the problem and the uniqueness of the solution. Due to the different characteristics of the building loads, this paper divides the total load into shiftable and fixed loads. Moreover, to model the building components and loads, grey-box modeling is adopted. Results show that employing a combination of TESS and BESS achieves peak load shaving while reducing 42.2% of the required BESS capacity compared with the case where the BESS is only used. In addition, the results indicate the effectiveness and robustness of the proposed algorithm.
Knowledge, Attitudes, and Practices concerning Black Fungus during COVID-19 Pandemic among Students of Bangladesh: An Online-Based Cross-Sectional Survey

Background: Infection with COVID-19 and its control entailing steroids and immunomodulatory medications disrupted normal immune function. The ensuing immunological disorder led to the rise of another infection—Black Fungus (Mucormycosis). However, the spread of Black Fungus can be minimized through proper knowledge, informed attitude, and conscious preventive practice. This study aimed to assess students’ knowledge, attitude, and practice (KAP) regarding Black Fungus amid the COVID-19 pandemic in Bangladesh. Methods: This cross-sectional study was carried out among Bangladeshi students from June to July 2021. Using Google Forms, an e-questionnaire was developed for this web-based survey, and the participants were selected through a snowball sampling approach. Results: Out of the 2009 participants, more than half were female (53.5%), and the majority were at an age between 18 and 25 years (31.5%) and had received higher secondary (HSC) schooling (77.8%), while around 61% resided in urban areas. Findings revealed that most of the students (63.8%) spent around 2 h on electronic and social media to become informed about COVID-19 and Black Fungus. Approximately 33% of the students showed low KAP scores (32.9%), whereas around 26% had high KAP scores. Our results show a significant association between KAP and sex, schooling, living status, residence, and media exposure. Conclusion: The knowledge of Black Fungus considerably varies among Bangladeshi students considering the place of residence, age, sex, living arrangement, and media exposure. Policymakers should emphasize awareness among people focusing on the results of this study to increase deterrent attitudes and protective practices to minimize the risks of being infected.
Exploration of Physicochemical Mechanism for Negative Bias Temperature Instability in GaN‐HEMTs by Extracting Activation Energy of Dislocations

The reliability issues caused by negative bias temperature instability (NBTI) impede the rapid expansion of gallium nitride (GaN)‐based high electron mobility transistors (HEMT). Clarifying the physicochemical reactions during the degradation process induced by NBTI is of paramount importance for tracing the origin of NBTI, so as to circumvent the intrinsic reliability problem. However, the investigation of deep‐seated reasons behind NBTI‐related issue still lacks a feasible approach and has not been explored in depth. This paper proposes a conjoint approach that combines electrical measurements, material analysis, and theoretical calculations to comprehensively investigate the molecular basis of degradation presented by NBTI. Extracting the activation energy of dislocations during stress effectively identifies the deep sources of device deterioration. The breaking of NH bonds and migration of hydrogen ions along the dislocations in GaN lattice are primarily responsible for the electrical instabilities according to the theoretical calculation of activation energy, which is also evidenced by material characterizations including transmission electron microscopy, X‐ray photoelectron spectroscopy and Raman analysis. Also, a molecular‐level model that demonstrates the reaction kinetics is well established for the fundamental understanding of the pivotal physicochemical mechanism behind NBTI, thus providing the direction for further optimization of GaN electronics.