Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Dexamethasone on IL-6 Levels in Confirmed COVID-19 Patients Treated at RSUP Dr. M. Djamil Padang

Background: Elevated IL-6 levels have been found in COVID-19 patients and are associated with a poor prognosis. According to COVID-19 management guidelines, several types of corticosteroids can be used as therapy modalities for COVID-19 patients, including dexamethasone, methylprednisolone, and hydrocortisone. The purpose of this study was to examine how dexamethasone administration affected changes in IL-6 levels in confirmed COVID-19 patients at RSUP Dr. M. Djamil Padang.Methods: This was a retrospective cohort study with a sample of all COVID-19 patients who met the inclusion and exclusion criteria and were treated in the COVID-19 isolation ward at RSUP Dr. M. Djamil Padang. The study began in June 2021 and concluded in July 2022. The data was analyzed both descriptively and analytically. The distribution of frequencies and proportions of each variable was included in the univariate analysis. Bivariate analysis employs data-scale-appropriate statistical tests such as the T-test to determine the relationship between independent and dependent variables.Results: The characteristic of the patients were mostly 18-49 years old (37.22%), female (55.67%), of severe clinical degree (49.44%), had no comorbidities (52.78%) and the majority (77.78%) received dexamethasone in the recommended dose (1 x 6 mg). The study's findings revealed that there was no difference in IL-6 values before and after dexamethasone administration in patients with moderate clinical degrees, but there were differences in IL-6 values before and after dexamethasone administration in patients with severe and critical clinical degrees.Conclusion: The IL-6 level has significantly decreased following dexamethasone administration. Dexamethasone administration causes significant changes in IL-6 values in severe and critical degrees but not in moderate clinical degree.
Research on Promotion Strategies for Social Robots

: In recent years, “computational propaganda” based on big data, social bot technology, and artiﬁcial intelligence has been widely used in a series of major events such as the US election, Brexit, and the Russia–Ukraine conﬂict, drawing attention from all walks of life around the world. This paper discusses and analyzes two dimensions of computational propaganda: propaganda strategies and technological methods. Based on these discussions and analyses, four tools for regulating computational propaganda are proposed.
Advancing parity is associated with high milk production at the cost of body condition and increased periparturient disorders in dairy herds

The objectives of this study were to determine the effects of parity on milk production, body condition change, periparturient health, and culling in Korean dairy herds. The data utilized included; milk yield, body condition score, cow parity, calving condition, periparturient disorders, culling, and reproductive status, which were recorded from 1290 calvings in eight dairy herds. The mean milk yield in cows over 305 days increased with increasing parity (p < 0.01). Cows with parities of 3, 4, and 5 or higher lost more body condition than those with a parity of 1 during month 1 of lactation (p < 0.01), and body condition recovery by cows with parities of 4 and 5 or higher was slower (p < 0.01) than recovery by cows with parities of 1, 2, or 3 until month 3 of lactation. The risk of retained placenta, metabolic disorder, and endometritis also increased with advancing parity (p < 0.05). Moreover, the incidence of ovarian cysts was lower in cows with a parity of one than in cows with greater parities (p < 0.01). Culling rate due to reproductive failure also increased with advancing parity (p < 0.01). These results suggest that parity increases milk yield, body condition loss during early lactation, the risk of periparturient disorders, and culling due to reproductive failure in dairy herds.
I-Theory: A Unifying Quantum Theory?

This paper gives an overview of a new theory called the “I-Theory”, in the context of several accepted theories in physics and cosmology. This paper reviews the salient features of the “I-Theory”, which introduces new particles like I, S∞ and A1 particles and provides a fuller understanding about Dark Matter, Dark Energy, Matter/Anti Matter and the four fundamental forces. “I-Theory” introduces a new concept of the “quality” of energy with White, Black and Red matter formed with regard to the frequency level of the energy vibration. The authors discuss the main features and controversies of the Standard Model, General Relativity, Big Bang, and Supersymmetry and attempt to answer some of the unsolved questions. It is proposed that the “I-Theory” can successfully encompass all major theories and thereby becomes the theory of the whole, the Unifying Theory.
Evaluation of the mental health status of community frontline medical workers after the normalized management of COVID-19 in Sichuan, China

Background During the coronavirus disease 2019 (COVID-19) pandemic, community medical workers, as the primary enforcers of community control measures, undertook many tasks with high exposure risk, resulting in severe psychological pressure, anxiety, depression and other psychological problems. Gender, type of workers, education, marital status, working years and other demographic factors were affect the mental state of medical workers. Community frontline medical workers gradually returned to normal work and life after the normalized management of COVID-19, but heavy work and high psychological pressure may continue to affect them. Thus, our research team used the same psychological questionnaire to investigate the psychological status of community frontline medical workers after the normalized management of COVID-19 compared with the COVID-19 period. Methods This was a cross-sectional study of community frontline medical workers in Sichuan, China, from February 6 to 17, 2023. Symptom Checklist-90 (SCL-90) and a self-designed questionnaire of demographic characteristics were provided to the participants point-to-point through a mobile network platform. Multiple logistic regression was used to analyze influencing factors related to community frontline medical workers’ psychology. Results A total of 440 valid questionnaires were statistically analyzed, including 192 (43.64%) from doctors and 248 (56.36%) from nurses. There were 222 (50.45%) participants who were SCL-90 positive. The median total SCL-90 score of medical workers was 105.0 (IQR 95.00–123.75), which was higher than that during the COVID-19 period. The doctor’s median SCL-90 score was 108.5 (IQR 96.00–136.25), and the positive item score was 16.5; the nurse’s median score was 104.0 (IQR 94.00–119.50), and the positive item score was 12.0. Bachelor’s degree education, no fixed contract and working years (10–19 years, 20–29 years, 30–39 years) were independent influencing factors for community frontline medical workers’ psychology. Conclusion After the normalized management of COVID-19, community frontline medical workers still suffered from psychological problems that were even more serious than those during COVID-19. Doctors were more likely to have psychological problems than nurses. In addition, the mental health status of community frontline medical workers was affected by education, type of contract and working years. Managers should pay attention to the mental health of these people.
Globally optimal image segmentation incorporating region, shape prior and context information

Accurate image segmentation is a challenging problem in the presence of weak boundary evidence, large object deformation, and serious mutual influence between multiple objects. In this thesis, we propose novel approaches to multi-object segmentation, which incorporates region, shape and context prior information to help overcome the stated challenges. The methods are based on a 3-D graph-theoretic framework. The main idea is to formulate the image segmentation problem as a discrete energy minimization problem. The prior region, shape and context information is incorporated by adding additional terms in our energy function , which are enforced using an arc-weighted graph representation. In particular, for optimal surface segmentation with region information, a ratio-form energy is employed, which contains both boundary term and regional term. To incorporate the shape and context prior information for multisurface segmentation, additional shape-prior and context-prior terms are added, which penalize local shape change and local context change with respect to the prior shape model and the prior context model. We also propose a novel approach for the segmentation of terrain-like surfaces and regions with arbitrary topology. The context information is encoded by adding additional context term in the energy. Finally, a co-segmentation framework is proposed for tumor segmentation in PET-CT images, which makes use of the information from both modalities. The globally optimal solution for the segmentation of multiple objects can be obtained by computing a single
Topology optimization for piezoresistive nanomechanical surface stress sensors in anisotropic 〈111〉 orientations

Microelectromechanical systems (MEMS)-based piezoresistive nanomechanical sensors are compact sensing platforms widely employed in vapor sensing, environmental monitoring, and biosensing. Despite their extensive utility, their lower sensitivity relative to their optical readout counterparts has been a limiting factor, constraining the wider application of this technology. Prior research has suggested that alternative silicon orientations, such as 〈111〉 orientations in (110) wafers, can significantly improve the sensitivity of piezoresistive sensors. However, the complexity of optimizing two-dimensional stress distribution and handling anisotropic elasticity has made device design a formidable task, leaving this promising avenue largely unexplored. To address this challenge, we employ density-based topology optimization to generate a series of optimized designs for piezoresistive nanomechanical sensors manufactured along 〈111〉 orientations. The properties of the immobilization layer—the functional coating on the sensor—are parametrically varied to explore optimal designs. Our study reveals a transition in optimized designs from a double-cantilever configuration to a suspended platform configuration, dictated by the stiffness ratio between the immobilization layer and the silicon layer. This transition is attributed to the shift in the neutral plane and the prevailing stress relaxation mechanism. In addition, we scrutinize the effects of piezoresistor geometry and find that the optimized designs depend asymmetrically on the piezoresistor position, a characteristic stemming from the anisotropic elasticity in 〈111〉 orientations. These optimized designs, verified by finite element analysis (FEA), demonstrate a notable improvement in sensitivity of more than 20% when benchmarked against traditional rectangular designs and equivalent optimized designs in conventional orientations, thereby validating the effectiveness of the present model. This study provides crucial knowledge for the design of piezoresistive biosensors, facilitating more efficient geometric design in future sensor development.
DESENVOLVIMENTO DE BARRA PROTEICA COM EXTRATO DE CAFÉ VERDE.

O interesse na producao e consumo de alimentos contendo compostos bioativos tem aumentado devido aos seus beneficios para a saude. O objetivo deste trabalho foi desenvolver barras de cereais acrescida de whey protein e extrato soluvel de cafe verde, com o intuito de elevar a composicao proteica e de antioxidantes. Realizou-se a analise do potencial antioxidante das barras e um teste piloto de analise sensorial. Concluiu-se que a modificacao trouxe maior teor de compostos antioxidantes quando comparado a barra controle. Link: https://eventos.textolivre. org/moodle/mod/forum/discuss.php? d=248
Assessment of manual operation note documentation practice: a cross-sectional study

Background: Operation note documentation captures the key findings and subtle elements of a surgical strategy and is crucial for patient safety. Poor operation note documentation can negatively influence postoperative patient care. This study aimed to assess manual operation note documentation practice. Methods: An institutional-based, cross-sectional study was conducted from 30 March to 30 April 2022, on 240 operation notes of patient data. Data were entered and analyzed by SPSS version 20. According to the RCSE, the Royal College of Surgeons of England, the practice of operation note documentation was rated excellent for each variable when it met 100%, good if it met more than 50%, and poor if it met less than 50% of the operation notes of patient data. Results: All operation notes (n=240) were handwritten. The practice of manual operation note documentation was deemed excellent in two (7.69%), good in 18 (69.2%), and poor in six (23.1%). Residents wrote 84.2% of the operation notes and surgeons and assistants were identified in greater than 94% of the notes, while anesthesia team members were identified in 90.8%. Estimated blood loss was documented in 4.2% of the notes, and the closure technique was described in 64.2%. The operation note templates did not include antibiotic prophylaxis, runner nurse name, or gauze and instrument counts. The urgency of the surgery and time of documentation had a negative relationship, and the seniority of the operation note writer had a positive relationship with manual operative note documentation practice. Conclusions and recommendations: Compared to the standard, all operation note documentation was incomplete and below the standard. We recommend that this comprehensive and specialized hospital administrator implement a new format for operation notes that incorporates RCSE requirements.
: desarrollo de un sistema e-learning basado en estándares IMS

El trabajo ha consistido en la participacion en un proyecto "e-learning" previamente existente, el sistema . Este proyecto tiene una doble vertiente de investigacion y de desarrollo de productos software para educacion en la red y esta encuadrado en el Departamento de Sistemas Informaticos y Programacion de la Universidad Complutense de Madrid, bajo la direccion del Profesor Baltasar Fernandez-Manjon. Como proyecto de investigacion, tiene como objetivos prioritarios el desarrollo de una infraestructura y de un entorno que permita evaluar diferentes propuestas de estandarizacion. Mas concretamente, se pretende analizar el potencial de los estandares educativos en la construccion de entornos de aprendizaje personalizado, mediante un conjunto de sistemas que implementen la infraestructura basica (e.g. creacion, acceso y reutilizacion de los contenidos). Otro objetivo principal del desarrollo, y a la vez el mayor reto, es conseguir una base arquitectonica estable, robusta y modular. Dicha base debera permitir a corto plazo implementar y desarrollar el potencial de los estandares IMS y a largo plazo debera permitir que el sistema se adapte a las modificaciones de los estandares con facilidad (incluso ante el escenario de cambios muy bruscos, suceso posible dada la falta de madurez del dominio). Asi, el legado del presente trabajo al proyecto sera un sistema funcional y a la vez una base sobre la que asentar el trabajo futuro del proyecto. [ABSTRACT]The work performed has consisted in assisting in an already existing research project in the field of e-learning, the system. Besides a strong research vocation, the project is also involved in the development of software products related to virtual class environments. The project exists within the SIP Department from the Complutense University at Madrid, under the direction of professor Baltasar Fernandez-Manjon. The main objective of the initiative is the development of environments and infrastructures to allow the evaluation of diverse standardization proposals. More precisely, the pretension is to evaluate the potential benefits of standardization in the construction of personalized e-learning environments by means of systems that implement basic services (e.g. creation, navigation and reuse of content). Another main objective of this development (and the biggest challenge) is to obtain a stable, modular and robust architectural core. Such architecture should allow in the short term the implementation of IMS standards to their full potential while in the long term it should permit an easy adaptation of the system to whichever modifications the standards might suffer (that must include scenarios with severe modifications of the standards. Such scenarios are possible because the field isn’t mature yet). Thus, the present task should leave as a legacy for both a functioning e-learning system and a solid base on which future work can built on.
Structural Insights into Protein-Ligand Interactions of Small Leucine Rich Repeat Proteoglycans with a Large Number of Binding Partners: An Overview

Small leucine rich repeat proteoglycans (SLRPs) exist in the extracellular matrix. SLRPs contain tandem arrays of LRRs flanked by cysteine clusters at the both N- and C-termini. The extreme N- and/or C-termini contain low complexity sequences, glycosaminoglycan (GAG) chain and/or sulfated tyrosine residues in some members of SLRPs. The LRR solenoid structure may be divided into four parts consisting of a concave surface, an ascending surface, a convex surface, and a descending surface. SLRPs share many biological functions including collagen fibrillogenesis, signaling, innate immunity, and inflammation through the binding of various ligands. We undertake a comprehensive literature search of publications in order to make a list of ligands of SLRPs. We describe and discuss the interacting sites of SLRPs to binding partners. The protein-ligand interactions occur on not only the concave surface but also the ascending surface and the N- or C-terminal capping regions. In addition, the extreme N- and/or C-terminal regions with the GAG chains or sulfated tyrosine residues participate in ligand-interactions.
A Retrospective Study on the Three-Port Technique of Laparoscopic Common Bile Duct Exploration for the Management of Cholelithiasis and Choledocholithiasis

Background Laparoscopic cholecystectomy (LC) with laparoscopic common bile duct exploration (LCBDE) is convenient in treating cholelithiasis and choledocholithiasis due to its advantage of accelerated recovery. This retrospective study aimed to summarize the experience of cholelithiasis and choledocholithiasis treatment via three-port approach of LCBDE in Eastern China. Methods Patients diagnosed with cholelithiasis and choledocholithiasis between July 2019 and October 2021 were included. Patients who received LC+LCBDE+primary suturing of the common bile duct (CBD) via a three-port approach were assigned to the LCBDE-P group, and those who received LC+LCBDE+T-tube drainage of CBD comprised the LCBDE-T group. The measurement data were compared between the two groups. P-values <0.05 indicated statistical significance. Results A total of 88 patients were divided into two groups: LCBDE-P (n=50) and LCBDE-T (n=38). Multiple logistic regression analysis showed that LCBDE-P is associated with a shorter length of stay (OR=0.115, 95% CI: 0.040–0.329, P<0.001) and lower hospitalization costs (OR=0.120, 95% CI: 0.041–0.357, P<0.001). No significant differences between the two groups were detected in the operation time, intraoperative hemorrhage, clearance rate of CBD stones, postoperative liver function, and postoperative complications (P>0.05). Conclusion The three-port approach of LCBDE is a safe and feasible strategy for managing cholelithiasis and choledocholithiasis. Compared to LCBDE-T, LCBDE-P reduces the length of hospital stay and medical costs during hospitalization.
Maximum likelihood-based recursive least-squares estimation for multivariable systems using the data filtering technique

ABSTRACT For multivariable equation-error systems with an autoregressive moving average noise, this paper applies the decomposition technique to transform a multivariable model into several identification sub-models based on the number of the system outputs, and derives a data filtering and maximum likelihood-based recursive least-squares algorithm to reduce the computation complexity and improve the parameter estimation accuracy. A multivariable recursive generalised extended least-squares method and a filtering-based recursive extended least-squares method are presented to show the effectiveness of the proposed algorithm. The simulation results indicate that the proposed method is effective and can produce more accurate parameter estimates than the compared methods.
Opioid-Induced constipation: Pathophysiology, Treatment and Management

Opioid analgesics are increasingly used to alleviate severe acute and chronic pain. Opioid-induced constipation (OIC) is the most common side effect of opioid analgesia, which can have a significant effect on the person's quality of life and lead some to decrease or stop taking their analgesia. Although it is recommended that laxative therapy is commenced at the same time as opioid medication, many laxatives do not target the underlying cause of OIC and therefore may be ineffective. As with any patient with constipation, other causes should be identified and, where possible, modified to reduce the risk of OIC. Simple laxatives remain the first-line treatment, but their efficacy should be regularly reviewed. Newer opioid antagonist medication should be considered where simple laxatives are ineffective.
Biodiseño: el proyecto que educa a la naturaleza

El artículo explora el campo del biodiseño demostrando, a través de una revisión de la literatura científica y la ilustración de los experimentos de investigación de diseño desarrollados por los autores, cómo el diseño puede diseñar artefactos híbridos instruyendo a la naturaleza en la dirección del pensamiento de diseño.
Graph theoretic approach for calculation of new Banhatti indices VIA recent algebraic polynomials with a chemical application

In this article, we design new distance-based topological indices which are computed by a recent polynomial approach. Also, we present a chemical application on the suitability of these indices with some drugs used for the treatment of COVID-19 via QSPR analysis. Curvilinear regression models are obtained and analysed for the physico-chemical properties of the COVID-19 drugs. Our models and findings could aid in the development of new drugs for the treatment of COVID-19. GRAPHICAL ABSTRACT