bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of Lotrafilcon A and Senofilcon A Bandage Contact Lenses on Visual Outcome and Ocular Comfort after Photorefractive Keratectomy (Effects of Two Different Bandage Contact Lens Following PRK)
Authors: Yakar Konuralp 
Published: April 2024

Abstract

Purpose
To compare the efficacy of two different silicone hydrogel bandage contact lenses (BCLs) in terms of visual rehabilitation and ocular discomfort following photorefractive keratectomy (PRK).
Methods
This prospective study included 60 eyes of 30 patients who underwent bilateral PRK surgery to correct myopia and/or astigmatism refractive errors. Following surgery, Lotrafilcon A contact lenses were applied to the right eye and Senofilcon A material contact lenses were applied to the left eye. On postoperative day 5, when the BCLs were removed, subjective ocular symptoms of discomfort were evaluated on a scale of 0 to 10, where 0 indicated no discomfort and 10 indicated maximum discomfort. The postoperative spherical equivalents (SE) of both eyes were compared on 15th day and 1st month. Postoperative SE ≤ ± 0.50 diopters were accepted as emmetropia. The number of patients who achieved emmetropia was also compared on the 15th day and the 1st month postoperatively.
Results
The ocular discomfort scores did not differ significantly between the BCLs on postoperative 5-day time period (p > 0.05). However, a statistically significant difference was observed between the two lenses in terms of their SE values on the postoperative 15th day and 1st month (p < 0.05). The eyes fitted with Senofilcon A material lenses demonstrated better postoperative visual rehabilitation.
Conclusion
Although the ocular discomfort scores following PRK between the two BCLs did not differ significantly, the Senofilcon A lenses performed better performance on the goal of SE postoperatively.
Enhanced Biosynthesis of Coated Silver Nanoparticles using Isolated Bacteria from Heavy Metal Soils and their Photothermal- based antibacterial activity: Integrating Response Surface Methodology (RSM) Hybrid Artificial Neural Network (ANN)- Genetic Algorithm (GA) Strategies
Authors: Mechouche Meroua Safa , Merouane Fateh , Addad Ahmed , Karmazin Lydia , Boukherroub Rabah , Lakhdari Nadjem 
Published: April 2024

Abstract

This study explores the biosynthesis of silver nanoparticles (AgNPs) using the Streptomyces tuirus S16 strain, presenting an eco-friendly alternative to mitigate the environmental and health risks of chemical synthesis methods. It focuses on optimizing medium culture conditions, understanding their physicochemical properties, and investigating their potential photothermal-based antibacterial application. The S16 strain was selected from soil heavy metals to exploit its ability to produce diverse bioactive compounds. By employing the combination of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)-Genetic Algorithm (GA) strategies, we optimized AgNPs synthesis, achieving a significant 59.2% improvement in yield under specific conditions (Bennet's medium supplemented with glycerol [5g/L] and casamino-acid [3g/L] at 30°C for 72h). A detailed physicochemical characterization was conducted. Notably, the AgNPs were well dispersed, and a carbonaceous coating layer on their surface was confirmed using Energy-Dispersive X-ray Spectroscopy. Furthermore, functional groups were identified using Fourier-transform infrared spectroscopy, which helped enhance the AgNPs' stability and biocompatibility. AgNPs also demonstrated efficient photothermal conversion under light irradiation (0,2w/cm2), with temperatures increasing to 41.7°C, after 30minutes. In addition, treatment with light irradiations against E.coli K-12 model effectively reduced the concentration of AgNPs from 105 to 52.5µg/mL, thereby to enhancing the efficacy of silver nanoparticles in contact with the E. coli K-12 model
Evaluation of clinical and radiographic warning signs for prediction of oroantral communication following tooth extractions
Authors: Jurasek Alexandra , Farkas Nelli , Frank Dorottya , Kolarovszki Bela , Sandor Balazs , Radacsi Andrea , Szanto Ildiko , Katona Krisztian 
Published: April 2024

Abstract

Objectives
Oroantral communication (OAC) is a relatively common and mild complication of maxillary tooth extractions. Preoperative prediction of OAC can reduce treatment duration and prepare both operators and patients for the procedure. This study aims to identify alarming radiographic and clinical indicators that can predict OAC.
Methods
In this retrospective case-control study, a control group twice the size of the OAC group was established. Clinical data were collected, and measurements were conducted separately by two blinded observers on digital panoramic radiographs. Inter-rater reliability was assessed. The correlation between OAC and demographic data (age, sex), as well as various factors assessed on panoramic radiographs (including, but not limited to, the length of the root, root projection into the sinus, bone width, presence of mesial and distal adjacent teeth), was statistically evaluated.
Results
Inter-rater reliability was found to be excellent. Several factors were identified as potential predictors of OAC. According to our model, the strongest predictors were the distance between the cemento-enamel junction and marginal bone, extent of root projection into the sinus, presence of sinus recess around the roots, angulation, and absence of the mesial adjacent tooth.
Conclusions
Well-defined measurements on panoramic radiographs may aid in predicting OAC. Further prospective investigations are necessary to confirm these indicators and address factors related to clinical examination and operation.
Research on Nonlinear Robust Control Strategy of Active Voltage Support for Grid-forming Photovoltaics
Authors: KANG Zhongjian , Dongjian LU 
Published: April 2024

Abstract

With the integration of a large number of photovoltaic renewable energy into the power grid, on the one hand, it will lead to a decrease in the proportion of synchronous generators with large inertia in the power grid, and on the other hand, because photovoltaic needs to be connected to the grid through inverters and other devices containing a large number of power electronic components, the inertia of the power grid will be reduced, the robustness will become poor, and the system voltage will fluctuate. In order to solve the above problems, this paper proposes a voltage active support method based on the expansion state observer and the terminal sliding mode control strategy. Firstly, a grid-forming photovoltaic grid-connected microgrid model was built in Matelab/Smulink. Secondly, the voltage controller in the virtual synchronous generator control was redesigned according to the synchronous generator control and nonlinear robust control technology. By adding an extended state observer and terminal sliding mode control to the voltage controller, the purpose of active voltage support and improved control performance is realized. Through simulation analysis, the results show that when the photovoltaic output changes, the voltage controller based on the expansion state observer and terminal sliding mode control can not only realize the voltage regulation function, but also effectively reduce the overshoot of the system voltage, improve the voltage response speed, and enhance the system stability of the system when the photovoltaic output changes.
Policy innovation in the digital era: Computational text analysis of media narratives on smart metering in India
Authors: Goyal Nihit 
Published: April 2024

Abstract

Although novel policies will be especially important for addressing grand societal challenges, how the digital transition influences the ability of governments to adopt new policies remains unclear. In this study, I explore the relationship between digitization and policy innovation to understand how the two might be related. Specifically, I study media narratives in the case of the ongoing roll-out of smart metering in India to assess whether and how this instance of digital governance has been discussed in the news. To do so, I analyze 370 articles in the largest English language daily newspaper in India using topic modelling and sentiment analysis. The findings highlight the polysemic nature of smart metering, its ability to support highly (re-)configurable and targeted policy designs, as well as some resistance to this policy innovation thus far. I discuss the implications of the findings for theory and research on digital governance and policy innovation.
Feasibility, acceptability and preliminary effects of a social network-based, peer-led HIV self-testing intervention among men in two Ugandan fishing communities
Authors: Matovu Joseph KB , Namwama Aisha Twahiri , Kemigisha Linda , Taasi Geoffrey , Nakabugo Jennipher , Wandabwa Julius , Bogart Laura M , Fakier Nuraan , Wanyenze Rhoda K. , Musinguzi Joshua , Serwadda David 
Published: April 2024

Abstract

Background
Social network-based interventions can improve uptake of health interventions. However, limited evidence exists on their feasibility and acceptability in fishing community settings. We assessed the feasibility, acceptability and preliminary effects of a social network-based, peer-led HIV self-testing (HIVST) intervention among men in Uganda.
Methods
The PEer-led HIVST intervention for MEN (PEST4MEN) is a pilot intervention conducted among men (15 + years) in Kalangala and Buvuma island districts. Data were collected between July and September 2022. The intervention was implemented through 22 “peer-leaders” who were trained in HIVST use and distribution processes and requested to refer at least 20 members from their social networks. A total of 475 men were referred; of these, 400 met the study enrolment criteria. Enrolled men were administered a baseline questionnaire and requested to pick two kits from their peer-leaders. The intervention was deemed feasible if > 80% of the kits given to the peer-leaders were distributed to men within their social networks and acceptable if > 80% of those who received the kits used them to self-test for HIV. First-time HIV-positive testers were asked if they linked to HIV care. Data were analyzed using STATA version 16.0.
Results
Of 400 men, 272 (67.9%) were aged 18–34 years; 227 (56.7%) were engaged in fishing or fishing-related activities while 328 (82%) had ever tested for HIV. Of 361 men (90.2%) interviewed at follow-up, 355 (98.3%) reported that they received kits from their peer-leaders; 352 (99.1%) used them to self-test for HIV. Of those who self-tested, 339 (96.3%) returned used kits for re-reading. Fifty-one men (14.5%) had reactive self-test results; of these, 16 men (31.4%) were first-time HIV-positive testers. Of first-time HIV-positive testers, 14 men (87.5%) went for confirmatory HIV testing, seven men (50%) were confirmed as HIV-positive and five men (71.4%) were linked to HIV care.
Conclusion
Our peer-led HIVST intervention was feasible and acceptable and identified a sizeable proportion of previously undiagnosed HIV-positive men who were linked to HIV care. However, while these results are promising, we recommend additional research in a large study, preferably in other fishing community settings, prior to the roll-out of this intervention.
Trial registration
ClinicalTrials.Gov: NCT05685498 (retrospectively registered on January 17, 2023).
Intelligent Agent-based Metaheuristic for Predicting Vibration Response of a Catamaran Digital Twin
Authors: Castro Brenno Moura , Reis Marcelo de Miranda , Salles Ronaldo Moreira , Monteiro Ulisses A. , Gutiérrez Ricardo H. R. 
Published: April 2024

Abstract

Embedded structural health monitoring (SHM) systems are complex and costly. Mostly, the monitoring task can only be executed using a limited set of sensors due to the inaccessibility of measurement locations. On the other hand, intelligent agent-based metaheuristic approaches are an emergent tool in modelling complex systems. Therefore, this work proposes an integrating machine learning (ML) and agent-based metaheuristic framework for predicting (virtual sensing) vibration responses at unmeasured locations. This framework combines autonomous agents and a decision tree (ML model) to search for the optimal modal basis and vibration measurements to predict responses accurately at unmeasured locations. As a case study, virtual sensing of vibration responses on the main deck of a catamaran, using a sparse set of accelerometers, was performed. Those measurements were obtained during a sea trial. Statistical analysis was performed using the ANOVA test to evaluate the robustness and accuracy of the proposed methodology. In addition, three metrics in time and frequency domains were also used: Root Mean Square Error (RMSE), Time-Response Assurance Criterion (TRAC) and Frequency-Response Assurance Criterion (FRAC). Results showed significant accuracy in the prediction task. In summary, this framework is a viable solution for online structural health monitoring (SHM) applications without requiring a high computational cost.
Long-term inflow forecast using meteorological data based on long short-term memory neural networks
Authors: Zhao Hongye , Liao Shengli , Song Yitong , Fang Zhou , Ma Xiangyu , Zhou BinBin 
Published: April 2024

ABSTRACT

Long-term inflow forecasting is extremely important for reasonable dispatch schedules of hydropower stations and efficient utilization plans of water resources. In this paper, a novel forecast framework, meteorological data long short-term memory neural network (M-LSTM), which uses the meteorological dataset as input and adopts LSTM, is proposed for monthly inflow forecasting. First, the meteorological dataset, which provides more effective information for runoff prediction, is obtained by inverse distance weighting (IDW). Second, the maximal information coefficient (MIC) can adequately measure the degree of correlation between meteorological data and inflow; therefore, the MIC can distinguish key attributes from massive meteorological data and further reduce the computational burden. Last, LSTM is chosen as the prediction method due to its powerful nonlinear predictive capability, which can couple historical inflow records and meteorological data to forecast inflow. The Xiaowan hydropower station is selected as the case study. To evaluate the effectiveness of the M-LSTM for runoff prediction, several methods including LSTM, meteorological data backpropagation neural network (M-BPNN), meteorological data support vector regression (M-SVR) are employed for comparison with the M-LSTM and six evaluation criteria are used to compare its performance. Results revealed that M-LSTM outperforms other test methods in developing the long-term prediction method.
Multivariate regression studies for the investigation of the COD, BOD, and TOC concentrations in the water of the Styr River within the zone of influence of the power plant discharge
Authors: Kuznietsov Pavlo M. , Biedunkova Olha O. 
Published: April 2024

Abstract

The discharge of cooling water into surface waters containing residual heat energy is a pressing issue in the environmental impact of nuclear power. Cooling water flow can cause negative changes in the water and ecological processes of natural waters, in particular an imbalance of organic matter, in combination with a complex of natural factors. The purpose of the article was to perform a statistical analysis of the dynamics of organic matter content in the water of the Styr River in the Rivne Nuclear Power Plant (Rivne NPP) impact zone to assess the manifestation of the environmental impact of the discharge of cooling water with residual heat. The monitoring data series consisted of the results of monthly monitoring of the rate of chemical oxygen demand, the rate of biochemical oxygen demand in 5 days, and total organic carbon in the Styr River sections before and after the discharge of Rivne NPP cooling water in 2018–2022.
Evaluation of Melatonin and its Nanostructures Effects on Skin Disorders Focused on Wound Healing
Authors: Mousavi Seyedeh Mohaddeseh , Etemad Leila , Yari Davood , Hashemi Maryam , Salmasi Zahra 
Published: April 2024

:
Skin is the largest organ of the human body functioning as a great primitive defensive
barrier against different harmful environmental factors. However, it is damaged through varying
injuries such as different wounds, burns, and skin cancers that cause disruption in internal organs
and essential mechanisms of the body through inflammation, oxidation, coagulation problems,
infection, etc. Melatonin is the major hormone of the pineal gland that is also effective in skin disorders
due to strong antioxidant and anti-inflammatory features with additional desirable antiapoptotic,
anti-cancer, and antibiotic properties.

:
However, melatonin characteristics require improvements due to its limited water solubility, halflife
and stability. The application of nanocarrier systems can improve its solubility, permeability,
and efficiency, as well as inhibit its degradation and promote photostability. Our main purpose in
the current review is to explore the possible role of melatonin and melatonin-containing nanocarriers
in skin disorders focused on wounds. Additionally, melatonin’s effect in regenerative medicine and
its structures as a wound dressing in skin damage has been considered.
Targeting the Interplay of Proteins through PROTACs for Management Cancer and Associated Disorders
Authors: Kumar Prashant , Sharma Himanshu , Singh Abhishek , Durgapal Sumit , Kukreti Gauree , Bhowmick Mithun , Bhowmick Pratibha , Ashique Sumel 
Published: April 2024

Abstract:
Cancer remains a formidable global health challenge, necessitating innovative therapeutic
strategies. Traditional small-molecule inhibitors often face limitations in selectively targeting diseaseassociated
proteins, leading to side effects and incomplete therapeutic responses. Proteolysistargeting
chimeras (PROTACs) turned out to be a promising approach to address these challenges.
Unlike traditional inhibitors, PROTACs leverage the cellular ubiquitin-proteasome system to selectively
degrade disease-associated proteins. In this review, we addressed PROTACs: Targeted approach
for cancer management, key findings, limitations, and future perspectives. For this, the authors
have critically reviewed literature obtained from prime sources comprising Google Scholar,
Web of Science, PubMed, and Publons. From the selected papers' reference section, a number of pertinent
articles were retrieved. Preclinical studies and early-phase clinical trials have demonstrated the
efficacy and potential of PROTACs in cancer management. Additionally, the potential of PROTACs
in overcoming therapy resistance, tackling tumor heterogeneity, and engaging multiple pathways is
explored. As research advances, addressing challenges and refining PROTAC technology will pave
the way for their integration into the next generation of cancer therapeutics, marking a transformative
era in precision medicine.
Cardiovascular Reflexes - Vagus as the Key Player
Authors: Herlekar Shantala S. , Doyizode Ashwini R. , Siddangoudra Savitri P. , V. Anupama 
Published: April 2024

Abstract::
The cardiac and vascular systems work in coordination by activating various reflex
mechanisms based on the body’s needs. These may be during physiological variations or pathophysiological
changes seen in disease conditions of varying degrees of severity. This article intends
to explain various reflexes involved in the homeostasis of the cardiovascular system and
the role of vagus as the key component in all these reflexes. The article also explains the components
of the reflex arc, the stimulus and response, and the role of reflex in a few diseases. This
article describes 22 different cardiovascular reflexes in detail.
Quality by Design-driven Analytical Method: A Quality Risk
Management-Based Liquid Chromatography Method for Daclatasvir and
Characterization of its Putative Degradants by LC-MS/MS
Authors: Chaturvedi Prashant , Chopra Shruti , Joshi Kalyani , Tauro Savita 
Published: April 2024

Background::
Antiviral drugs can cure more than 95 percent of people with hepatitis
C, but the inaccessibility of quality affordable medicines and the lack of their uninterrupted
supply poses a major challenge. Impurities in drugs have a significant impact on their quality
and are one of the substantial causes of drug recalls, ultimately leading to the unavailability of
the drug in the market. Hence, there is a need for a robust, quality risk management and quality
by design-driven analytical method that can detect the antiviral drug, Daclatasvir dihydrochloride,
in the presence of its probable impurities.

Objective::
This study aimed to develop a Quality by Design-driven stability- indicating liquid
chromatography method for Daclatasvir dihydrochloride and the characterization of its putative
degradants by LC-MS.

Method::
The fishbone diagram and quality risk assessment investigated twenty-four process
parameters and concluded that three risk parameters, i.e., flow rate, buffer pH, and stationary
phase type, were the critical process parameters. The critical quality attributes viz. resolution
between impurity 6 and DCV and impurity 2 & 3 (Rs˃1.5), the shape of the peak of DCV
which is decided by the Number of Theoretical Plates (NTP˃5000), and the retention time of
Daclatasvir (tR14-23 mins) were optimized using a two-level three-factor full factorial design
with five center points.

Results::
The optimized method is stability-indicating in its true sense as it can separate the sample
with its degradants generated in basic (three), acidic (two), oxidative (H2O2: three, Azobisisobutyronitrile:
one), photo (three), and dry heat (one) conditions. Degradants structures were elucidated,
and degradation routes were established, using LC-MS and LC-MS/MS analyses.

Conclusion::
The drug is highly susceptible to acid, base hydrolysis, and oxidation degradation
conditions and poses a significant risk to the analytical method to fail in system suitability criteria.
Hence, a robust and flexible chromatographic method with the capacity for continuous
improvement was developed and successfully validated within the criteria of design space.