bluuebunny/crossref_metadata_2025 huggingface dataset:
Azithromycin: Antibiotic and not only...
Authors: Vizel A. A. , Vizel I. Yu. , Zalilova A. Sh.
Published: December 2024

Antibacterial therapy is an essential component of treatment for bacterial respiratory infections. The last decade has seen an increase in resistance of respiratory pathogens to different classes of antibiotics. The purpose of this review was to analyze publications on azithromycin, a macrolide with both antibacterial and non-antibacterial properties. Studies conducted worldwide indicate heterogeneity in both the frequency of azithromycin use and resistance to it, but in general, there is an increase in the values of its minimum inhibitory concentration against pathogens for which the use of macrolides is indicated. The COVID-19 pandemic has led to an expansion of clinical experience with the use of this azalide, demonstrating sufficient safety of the drug. However, recent large retrospective studies have shown the inappropriateness of its etiotropic use in this viral infection, limiting it only to proven cases of bacterial pneumonia caused by pathogens sensitive to macrolides. One of the ways to optimize the use of azithromycin is its combined use with other drugs that provide synergy in clinical effectiveness. An important property of azithromycin, ensuring its clinical effectiveness, is its ability to affect inflammatory processes in the respiratory tract, the state of the respiratory epithelium. The anti-inflammatory properties of azithromycin are used in various fields of medicine, including traumatology, gynecology and dentistry. The clinical effectiveness of antibiotics is determined by their rational use, patient compliance and convenience of the dosage form. In this regard, work is underway to create forms convenient for oral administration, to overcome the bitter taste. In this direction, certain successes have been achieved by domestic developers in the introduction of a dispersible form of azithromycin.
Mucoactive herbal drugs in complex therapy of acute respiratory diseases
Authors: Chichkova N. V.
Published: December 2024

The major etiological factors that influence the development of acute respiratory diseases include various viral and bacterial agents that possess a high tropism for the respiratory epithelium. These diseases are known to be medical conditions that are associated with similar clinical signs and symptoms, as well as a pronounced range of degrees of severity and development of the disease complications. Cough is a prevalent clinical symptom in the clinical picture of ARD. For the treatment of cough, it is advisable to use drugs that have a complex effect: expectorant, secretolytic, bronchospasmolytic and anti-inflammatory. Herbal drugs have a reflex expectorant effect. When taken orally, they irritate the stomach receptors, bronchial gland secretion is enhanced reflexively, the ciliated epithelium activity is increased, and the bronchial tree smooth muscle contractions are intensified. The sputum becomes thinner and is cleared from the lungs more easily. Combinations of two or more medicinal herbs that complement or enhance each other's action are often used. It should be noted that such combination makes it possible to achieve a complex therapeutic effect i.e. anti-inflammatory, as well as mucolytic and secretolytic. It is preferable to prescribe mucoactive herbal drugs at the initial stage of the disease due to their multicomponent action, as well as good tolerability and high efficiency. Both Bronchipret (syrup) and Bronchipret TP (tablets) fully possess all these properties. The article presents the main mechanisms of action of the active substances of the drug, discusses the main points of “application” of pharmacological activity in the treatment of acute respiratory diseases with cough, and shows new targets for its use, taking into account new experimental and clinical evidence on antiviral activity against SARS-CoV-2.
Targeted therapy for severe bronchial asthma: Phenotyping of patients and algorithm for selecting a genetically engineered biological drug
Authors: Naumova V. V. , Zykov K. A. , Valenta R. , Kovtun O. P. , Beltyukov E. K. , Smolenskaya О. S. , Bykova G. A. , Shtanova A. A.
Published: December 2024

Introduction. The “spot” effect of immunobiological drugs necessitates patients' selection based on pathogenetic mechanisms of the disease to ensure therapy effectiveness.

Aim. To determine characteristics of T2-asthma main phenotypes and develop an algorithm for selecting a first- and second-line biologics.

Materials and methods. Being retrospective and prospective in nature the research was directed at adult patients with severe asthma who received target therapy and were included in the registry of Sverdlovsk region. Cluster analysis made it possible to identify the most distinctive features of allergic, nonallergic eosinophilic and mixed SA. Pathogenetic mechanisms of T2 inflammation determined the choice of first-and-second-line biologics.

Results. Allergic phenotype is characterized by existence of allergy and first appearance of asthma before the age of 18, satellite allergic rhinitis and the Phadiatop test result ≥ 1,53 PAU/L. The features of non-allergic eosinophilic asthma are as follows: asthma first appearance at the age of 32 and older, eosinophilia ≥ 150 cells/gl, absence of allergy, satellite chronic rhinosinusitis polyposa (CRSP) and NSAIDs intolerance. The features of the mixed asthma are as follows: first appearance at the age of ≥ 18 and < 32 years old, allergy in combination with eosinophilia ≥ 300 cells/gl, AR and a positive Phadiatop allergy test result, CRSP and NSAIDs intolerance. It is the allergic phenotype of SA when preference should be given to anti-IgE drug. Dealing with non-allergic phenotype of SA one should consider anti-IL5 biologics more preferable. Taking into consideration Th2 and ILC2 ways in action mechanism it is possible to affirm that anti-IL4R therapy is effective in mixed asthma.

Conclusions. In real clinical practice the initial phenotyping of SA facilitates the correct choice of a first- and second-line targeted drug.
Electrochemically Activated Catholyte Solutions in Sanitization
Authors: Burykina E. , Kuzina Zhanna
Published: December 2024

Electrochemical activation of water or mineralized salts produces a neutral or acidic solution (anolyte) in the anode chamber and an alkaline solution (catholyte) in the cathode chamber. Yet the prospects of catholyte as an alkaline detergent remain understudied. This research featured such physicochemical properties of catholyte as pH, alkalinity, specific electrical conductivity, mineralization, water contact angle, and surface tension in distilled and tap water with different concentrations of sodium chloride during electrochemical activation. The alkalinity index of the catholyte in the tap water sample was by 7-10% higher than in the distilled water sample. A similar correlation was detected in the electrochemical activation of sodium chloride solutions in tap water. The pH, alkalinity, and specific electrical conductivity of the catholyte did not depend on the sodium chloride concentration. The alkalinity of the catholyte increased together the electrochemical activation time (60 min). The water contact angle proved to be an important indicator for prospective detergents. In this study, different substances with complexing and wetting properties made it possible to reduce the water contact angle of the catholyte solutions by 50-60% and their surface tension by 15-20%. Catholytes mixed with substances with complexing and wetting abilities demonstrated good prospects as detergents for dairy processing equipment, except for heat exchange devices.
Improved daily streamflow forecasting for semi-arid environments using hybrid machine learning and multi-scale analysis techniques
Authors: Difi Salah , Heddam Salim , Zerouali Bilel , Kim Sungwon , Elmeddahi Yamina , Bailek Nadjem , Augusto Guimarães Santos Celso , Abida Habib
Published: December 2024

This study aimed to improve daily streamflow forecasting by combining machine learning (ML) models with signal decomposition techniques. Four ML models were hybridized with five families of maximum overlap discrete wavelet transforms (MODWTs). The hybrid models were applied to predict daily streamflow at the Bir Ouled Taher station in northern Algeria. Model performance was evaluated using multiple statistical metrics and compared to standalone ML models. The hybrid MODWT-Gaussian process regression (GPR) model using Symlet wavelets (MODWT-GPR3 sym4) achieved the best performance, with R = 0.99 and NSE = 0.98 during validation. This significantly outperformed the standalone models tested and other hybrid combinations. The MODWT-GPR3 sym4 model demonstrated a superior ability to capture nonlinearities and predict peak flows. Hybridization of ML models with wavelet transforms, particularly the MODWT-GPR approach, can substantially improve daily streamflow prediction accuracy compared to standalone models. However, model performance may vary between watersheds due to differences in hydrological characteristics. Consideration of catchment concentration time when selecting model inputs could further enhance forecasting capabilities.
Cluster analysis-based hydrological similarity assessment in small watersheds of Shandong Province's hilly areas
Authors: Li Dongyun , Sang Guoqing , Liu Wei , Liu Yang , Zhao Mengjie
Published: December 2024

The clustering of small watersheds based on hydrological similarity serves as an effective technique for identifying similarities in watershed runoff generation and routing conditions. It also addresses the challenge of parameter transplantation in undocumented areas. In this research, 545 small watersheds in hilly areas within Shandong Province were studied using 22 selected indicators to represent their climate and underlying surface characteristics. The study employed a two-stage clustering method combining the self-organizing map (SOM) neural network with the K-means algorithm, facilitating the classification of these watersheds into various groups. Each group of small watersheds was then analyzed for its unique characteristics. To validate the reasonableness of the classification results, the flood peak modulus of each watershed was calculated using a hydrologic-hydraulic method, while a parameter transplantation study was carried out and generalized for the clustering results. The findings indicate that the SOM-K-means clustering method efficiently classified the watersheds into 12 similar groups, validating its effective application in small watershed classification. This classification assists in solving the problem of flood forecasting in the ungauged watersheds in Shandong Province and developing more effective flood risk management strategies.
Insight into groundwater level prediction with feature effectiveness: comparison of machine learning and numerical methods
Authors: Goodarzi Mohammad Reza , Bafrouei Hamidreza Bana , Vazirian Majid
Published: December 2024

Reliable simulation of groundwater fluctuation is imperative for implementing sustainable groundwater management so that not only local people will be covered, but also environmental threats are going to be restrained in the future. This research aims to scrutinize the accuracy of numerical model (NM) and machine learning (ML) methods for groundwater level prediction (GWLP) for Yazd-Ardakan Plain from 2012 to 2019 in monthly steps. Additionally, principal component analysis (PCA) is applied to investigate the impact of each ML input feature on GWLP. The study area's aquifer data were analyzed and prepared to develop the conceptual model of MODFLOW and train ML algorithms for GWLP. Considering observation wells (OBWs), operation wells (OPWs), and their latitude and longitude as input features in convolutional neural networks (CNN), support vector machine (SVM), and decision tree (DT) algorithms, GWLP was performed. The results demonstrate that although MODFLOW considers the unique features of the aquifer, the most accurate GWLP was achieved by SVM, with root mean square errors (RMSE), correlation coefficient (), and area under the receiver operating characteristics (ROC) curve (AUC) values of 0.12, 0.90, and 0.94, respectively. Furthermore, PCA presented that the observed groundwater level (GWL) was the most effective feature with 71%.
Geochemical assessment of groundwater quality using Nemerov's pollution index: a micro-level study in the groundwater critical zone of western Rajasthan, India
Authors: Parihar Sangeeta , Jadhav Raina , Pachak Suresh Kumar , Solanki Vijendra Singh , Alarifi Saad S. , Yadav Krishna Kumar , Agarwal Neha , Ghosh Tathagata , Yadav Virendra Kumar
Published: December 2024

The pollution index is a helpful tool for assessing the quality of groundwater. To assess the water quality in the southern segment of Barmer District (Rajasthan), India, we collected 20 samples of groundwater from the post-monsoon 2021 and pre-monsoon 2022 periods. Physicochemical parameters such as pH, electrical conductivity (EC), total hardness, Cl−, SO4--, F−, NO3−, total dissolved solids, Ca2+, and Mg2+ were analyzed. To better understand the spatial and temporal variations, maps were generated in the Geographic Information System (GIS) environment in association with the seasonal correlation matrix. Nemerov's index method was used for determining the pollution level of groundwater sources. The results showed that there was significant spatial and temporal variation in the concentration level of physicochemical parameters. The correlation matrix revealed that the level of positive correlation among the parameters was higher during the pre-monsoon period of 2022 compared to the post-monsoon period of 2021. The result was evaluated using the standard set by the World Health Organization and the Bureau of Indian Standards. According to the result obtained from Nemerov's index technique, most parameters were within safe conditions in both seasons except for EC and NO3-. The results indicated that the improved Nemerov index technique can represent the status of groundwater more accurately.
Climate change and waterborne diseases in temperate regions: a systematic review
Authors: Salubi Eunice A. , Gizaw Zemichael , Schuster-Wallace Corinne J. , Pietroniro Alain
Published: December 2024

Risk of waterborne diseases (WBDs) persists in temperate regions. The extent of influence of climate-related factors on the risk of specific WBDs in a changing climate and the projections of future climate scenarios on WBDs in temperate regions are unclear. A systematic review was conducted to identify specific waterborne pathogens and diseases prevalent in temperate region literature and transmission cycle associations with a changing climate. Projections of WBD risk based on future climate scenarios and models used to assess future disease risk were identified. Seventy-five peer-reviewed full-text articles for temperate regions published in the English language were included in this review after a search of Scopus and Web of Science databases from 2010 to 2023. Using thematic analysis, climate-related drivers impacting WBD risk were identified. Risk of WBDs was influenced mostly by weather (rainfall: 22% and heavy rainfall: 19%) across the majority of temperate regions and hydrological (streamflow: 50%) factors in Europe. Future climate scenarios suggest that WBD risk is likely to increase in temperate regions. Given the need to understand changes and potential feedback across fate, transport and exposure pathways, more studies should combine data-driven and process-based models to better assess future risks using model simulations.
Review of policies for building climate resilience in urban sanitation systems and services in Nepal
Authors: Manandhar-Sherpa Anjali , Lüthi Christoph , Howard Guy
Published: December 2024

Building resilience of sanitation systems and services to changing climate is necessary to ensuring continuity of essential sanitation services in the face of hazardous events. An approach towards this solution is to create a robust enabling environment through policies for planning and implementing adaptation measures. The objective of this study is to review the climate change and water and sanitation policies in Nepal to assess the adequacy of the documents in addressing climate resilience in sanitation systems and services, with focus on urban on-site sanitation. This paper is based on desk review of existing policy and plan documents related to climate change and water, sanitation, and hygiene (WASH) in Nepal. The review has revealed that sanitation and climate change are addressed only to a limited extent, while focus on urban on-site sanitation is inadequate. These documents are therefore insufficient to provide guidance to build resilience in urban on-site sanitation systems and services. The paper concludes that sanitation-specific policies and programmes can tackle the sanitation challenges more effectively and address climate-related vulnerabilities in urban on-site sanitation systems and services. Enhanced collaboration among different stakeholders can create an environment conducive for policy changes and investments.
Evaluating extreme climatic indices in a mountainous region using satellite data: a framework to advance climate resilience and sustainable development
Authors: Mbalibulha Ezira , Wasswa Joseph , Obura Denis
Published: December 2024

Batwa communities inhabiting Uganda's complex topography lack timely access to climate information services (CISs) due to scarce meteorological stations. Tailoring historical satellite data to local spatio-temporal dimensions is central to catalyzing sustainable development goals (SDGs). This study employed satellite data from 1983 to 2023 to inform sustainable and climate-smart decision-making for the Batwa communities in southwestern Uganda. Daily precipitation (DPR) and air temperature data were obtained from the Climate Hazard Group Infrared Precipitation with Station (CHIRPS) and the NASA Power website (NASAPW), respectively. ClimPACT3 model was employed to generate climate indices (CIs) while R-Instat was utilized to perform statistical analysis. Results revealed that the area experiences wetter than normal conditions, with precipitation (PR) ranges of 1,006–1,489 mm. A significant (p-value < 0.05) increase in the annual sum of daily precipitation (ASDPR) > the 95th percentile was observed. The daily minimum temperature (DTMin.) and maximum temperature (DTMax.) ranged from 13.1 to 14.5 °C and 24.0 to 26.4 °C, respectively. The current study connected the implications of the results to the broader SDGs. The synergies between SDG 13 and other SDGs were also established. Therefore, the study's outcomes are imperative in advancing climate resilience for the agricultural, health, and water resources sectors.
A review of biogas upgrading technologies: key emphasis on electrochemical systems
Authors: Mohammadpour Hossein , Cheng Ka Yu , Pivrikas Almantas , Ho Goen
Published: December 2024

Biogas, consisting mainly of CO2 and CH4, offers a sustainable source of energy. However, this gaseous stream has been undervalued in wastewater treatment plants owing to its high CO2 content. Biogas upgrading by capturing CO2 broadens its utilisation as a substitute for natural gas. Although biogas upgrading is a widely studied topic, only up to 35% of produced raw biogas is upgraded in the world. To open avenues for development research on biogas upgrading, this paper reviews biogas as a component in global renewable energy production and upgrading technologies focusing on electrochemically driven CO2 capture systems. Recent progress in electrochemical CO2 separation including its energy requirement, CO2 recovery rate, and challenges for upscaling are critically explored. Electrochemical CO2 separation systems stand out for achieving the most affordable technology among the upgrading systems with a low net energy requirement of 0.25 kWh/kg CO2. However, its lower CO2 recovery rate compared to conventional technologies, which leads to high capital expenditure limits the commercialisation of this technology. In the last part of this review, the future perspectives to overcome the challenges associated with electrochemical CO2 capture are discussed.
Technical and financial evaluation of an emerging thermochemical technology for sustainable management of municipal wastewater sludge
Authors: Zvimba John Ngoni , Musvoto Eustina , Kholisa Buyisile
Published: December 2024

The management of municipal wastewater sludge is a significant challenge for wastewater management, particularly the need to manage and dispose of the sludge in an environmentally friendly and sustainable manner. The emergence of stricter regulations regarding landfill disposal of wastewater sludge necessitates the need for alternative options for municipal wastewater sludge management, with thermochemical technologies potentially contributing towards achieving carbon neutrality goals and fostering sustainable development. This study sought to address these challenges through a technical and financial evaluation of a pilot-scale emerging thermochemical technology, the enhanced hydrothermal polymerization to provide adequate understanding of the technology's feasibility regarding its application for municipal wastewater sludge volarization into a multi-use hydrochar. The study findings indicated that the enhanced hydrothermal polymerization-generated hydrochar exhibited significant energy content compared to wastewater sludge, suggesting the potential use of the hydrochar as an energy source. The preliminary designs of a full-scale greenfield installation and retrofit processing 50 t/d and 35 t/d dry sludge, respectively, were evaluated to be technically feasible. Furthermore, on the basis of preliminary designs, the enhanced hydrothermal polymerization technology was determined to be the most financially feasible option, also offering other unique advantages over well-established technologies currently used within municipal wastewater services.
Systematic bibliographic analysis of heavy metal remediation
Authors: Chen Shan , Ding Yuanzhao
Published: December 2024

Heavy metals pose a significant threat to human health, with contaminated water sources linked to severe conditions, including gastric cancer. Consequently, the effective remediation of heavy metals is crucial. This study employs a bibliographic analysis to examine key methodologies, leading organizations, and prominent countries involved in heavy metal remediation. By systematically reviewing around 1,000 records, the paper identifies the most critical remediation techniques and provides a comprehensive overview of current practices in the field. Additionally, the study explores prospects, emphasizing the potential of emerging technologies such as big data and machine learning to enhance remediation efforts. It highlights recent advancements, identifies significant trends, such as the growing use of bioremediation and nanotechnology, and addresses critical challenges in the remediation landscape, including regulatory hurdles and technological limitations. By making stronger connections between the identified trends and their implications for future research, this comprehensive analysis aims to provide valuable insights and guide the development of improved strategies for mitigating the impact of heavy metal contamination, ultimately safeguarding public health.
Exploring the Gut-Atrial Fibrillation Link: A Comprehensive Review
Authors: Abdallah Mohammad , Othman Rima , Antar Gaelle , Atwi Maya , Butrus Tony , Hajjar Elias , Jaafar Osamah , Machrekeki Marita , Mikhael Eddy , Rbeiz Tatiana , Swesa Jessica , Alani Firas , Zaatar Muriel Tahtouh
Published: December 2024

:

This literature review explores the correlation between atrial fibrillation (AF) and the gut microbiome by elucidating its significance in
cardiovascular health. AF stands as a prevalent cardiac arrhythmia associated with increased morbidity and mortality rates worldwide. The gut
microbiome, a complex ecosystem of microorganisms inhabiting the gastrointestinal tract, plays a crucial role in systemic health through its
influence on immune modulation, metabolic processes, and host-microbe interactions. Emerging evidence suggests a potential link between AF
and alterations in gut microbial composition, raising intriguing questions about underlying mechanisms and clinical implications.

:

Recent investigations have shed light on the potential interplay between AF and gut microbial composition. Alterations in gut microbiota diversity
and abundance have been observed in AF patients compared to healthy controls, suggesting a possible link between gut dysbiosis and arrhythmia
susceptibility. Mechanistic studies propose several pathways through which gut microbial metabolites and immune modulation may influence atrial
electrophysiology and arrhythmogenesis.

:

The clinical implications of the AF-gut microbiome connection are profound. Microbiome-based biomarkers hold promise for risk stratification,
enabling early identification of individuals at elevated risk of AF development or recurrence. Furthermore, interventions targeting the gut
microbiome, such as probiotics, prebiotics, and dietary modifications, offer innovative therapeutic avenues for AF management, potentially
augmenting traditional treatment modalities.

:

Despite significant progress, challenges such as methodological limitations and the need for further validation in diverse patient cohorts remain
present. Longitudinal studies are warranted to elucidate the temporal relationship between gut microbiome alterations and AF onset or progression.
Nevertheless, understanding the AF-gut microbiome connection provides a foundation for personalized medicine approaches, optimizing AF
management and improving cardiovascular health outcomes.
Location Optimization of Logistics Distribution Center Based on Neural Network
Authors: Li Lianhui
Published: December 2024

Introduction:

The logistics distribution center is a hub that occupies the position of
“central nerve” in the whole logistics operation process. Choosing a reasonable logistics distribution
center can improve the operation efficiency of the whole logistics system and reduce the cost
of workforce and material resources.

Objective:

The goal of this work is to design a patented technology to optimize the location of the
logistics distribution center.

Methods:

In this patent study, the appropriate clustering algorithm and mathematical model to
plan the logistics distribution path are selected to solve the location problem of the distribution
center. The location data is input into the neural network model, and the score of the location
scheme is output. The mean-shift clustering algorithm is used to divide the geographical location
of the merchants, who need to distribute products, and an aggregated distribution area is obtained.
Then, a practical mathematical programming model is established to solve the location problem of
logistics transfer stations.

Results:

Comparing the clustering situation of three data point aggregation algorithms, the most
reasonable data point aggregation method is obtained. Finally, the data of the location scheme is
input into the neural network, and the most perfect logistics distribution location center is selected
according to the output score.

Conclusion:

The establishment of an intelligent logistics distribution center should not only be
based on the development needs of the logistics market but also on the development of modern Internet
of Things technology.
Analysis of Ultrasonic Transducer Characteristics and Ultrasound-assisted Flotation Test Device
Authors: Sun Liqing , Ma Shenghao , Zhang Zhihua , Gao Kuidong , Xu Meng , Liu Tianyu , Li Lin
Published: December 2024

Introduction:

The application of ultrasound in mineral flotation has broad prospects.
To further explore the mechanism of ultrasound in flotation, an ultrasound-assisted flotation experimental
device was designed, and the mass transfer characteristics of its ultrasound transducer
were analyzed.

Background:

Ultrasound has shown excellent application prospects in mineral flotation, but ultrasonic
flotation technology is still in the laboratory testing stage.

Objective:

An advanced experimental device designed and manufactured specifically for conducting
ultrasonic-assisted flotation experiments has been developed to accelerate the application of ultrasonic
flotation technology in industrial production and improve efficiency.

Method:

Firstly, the ultrasonic-assisted flotation test device's overall layout and material selection
will be determined. Next, structurally design the core component of the ultrasonic mass transfer
device, the piezoelectric transducer. Then, an ultrasonic mass transfer device simulation model
will be created, including a vibration simulation model and a sound pressure field simulation model.
Finally, a finite element analysis of the mass transfer characteristics of the ultrasonic transducer
will be conducted using simulation software.

Results:

The analysis of the ultrasonic transducer device's mass transfer characteristics yielded ideal
results, and based on the obtained data, the ultrasonic-assisted flotation test device was successfully
constructed.

Conclusion:

The optimal ultrasonic vibration frequency of the ultrasonic transducer designed in
this experiment is 21482 Hz, and both unilateral and bilateral ultrasound can generate regular and
stable ultrasonic pressure fields. At the same time and position, the device's excitation voltage
change positively correlates with the absolute value change of sound pressure amplitude. The experimental
data shows that the ultrasonic-assisted flotation test device has demonstrated feasibility
and reliability in practical applications.
Treating Burn Infections With Topical Delivery of Positively Charged Norfloxacin-Loaded Lipid-Polymer Hybrid Nanoparticles
Authors: Mohammed Kamilia H.A. , Rasslan Fatma , Abd El‑Fattah Marwa A. , Shawky Seham , Amin Omnya M. , Eassa Heba A.
Published: December 2024

Background:

Norfloxacin (NFX) is a wide-spectrum antibacterial agent that suffers
from low water solubility and first-pass metabolism. This diminishes its oral bioavailability by
60-70%.

Objective:

This work aims to formulate a topical gel of NFX-loaded lipid polymer hybrid nanoparticles
(NFX-LPHNPs) that combine the merits of liposomes and polymeric nanoparticles to overcome
these problems.

Methods:

NFX-LPHNPs formulations were developed using Precirol ATO (lipid) and Eudragit
RL100 (polymer). They were characterized for particle size, uniformity of distribution, entrapment
efficiency, zeta potential, and in-vitro release. Box–Behnken design was applied to study sequentially
different variables' impact on material attributes. Then the optimized formula was re-evaluated,
and incorporated in an HPMC-gel formulation. The gel formulation was evaluated for its
physical properties, in vitro-release, and antibacterial activity.

Results:

NFX-LPHNPs exhibited particle sizes ranging from 28.92 to 730.30 nm. Particles were
uniformly distributed with a positively charged surface (indicated by zeta potential with values
from +3.91 to +60.2 mV). Formulations showed a % cumulative drug release of 87.9-100% in 8 h.
The optimized formula showed a satisfied fit of measured-to-predicted responses with 159 nm particle
size, 92.61% release and 79.2% entrapment efficiency. Gel formulation showed a sustained
release over 24h. Antibacterial testing against Staphylococcus aureus, Acinetobacter baumannii
and Pseudomonas aeruginosa revealed enhanced activity of NFX-LPHNPs against these pathogens
compared to bare NFX loaded gel.

Conclusion:

These results illustrated the high potential of lipid-polymer hybrid nanoparticles to
improve NFX activity against resistant pathogens common in burn infections. Moreover, the topical
application helps overcome Norfloxacin oral-associated problems.
Non-Ionic Surfactant Vesicles (Niosomes): Structure, Functions, Classification and its Advances in Enhanced Drug Delivery
Authors: Padarthi PavanKumar , V Kalvimoorthi , Challa Ranadheer Reddy , Vallamkonda Bhaskar , Grandhe Neelima , Dogiparthi Lakshman Kumar , Kaliyaperumal Rajaganapathy
Published: December 2024

Niosomes, non-ionic surfactants, cholesterol, aqueous core, bilayer, hydrophilic drugs, hydrophobic drugs.
Efficacy and Safety of Natural Apigenin Treatment for Alzheimer's Disease: Focus on In vivo Research Advancements
Authors: Zhang Nan , Nao Jianfei , Dong Xiaoyu
Published: December 2024

Background:

Alzheimer's Disease (AD) is the most common dementia in clinics. Despite
decades of progress in the study of the pathogenesis of AD, clinical treatment strategies for AD remain
lacking. Apigenin, a natural flavonoid compound, is present in a variety of food and Chinese herbs and
has been proposed to have a wide range of therapeutic effects on dementia.

Objective:

To clarify the relevant pharmacological mechanism and therapeutic effect of apigenin on
animal models of AD.

Methods:

Computer-based searches of the PubMed, Cochrane Library, Embase, and Web of Science
databases were used to identify preclinical literature on the use of apigenin for treating AD. All databases
were searched from their respective inception dates until June 2023. The meta-analysis was performed
with Review manager 5.4.1 and STATA 17.0.

Results:

Thirteen studies were eventually enrolled, which included 736 animals in total. Meta-analysis
showed that apigenin had a positive effect on AD. Compared to controls, apigenin treatment reduced
escape latency, increased the percentage of time spent in the target quadrant and the number of plateaus
traversed; apigenin was effective in reducing nuclear factor kappa-B (NF-κB) p65 levels; apigenin
effectively increased antioxidant molecules SOD and GSH-px and decreased oxidative index
MDA; for ERK/CREB/BDNF pathway, apigenin effectively increased BDNF and pCREB molecules;
additionally, apigenin effectively decreased caspase3 levels and the number of apoptotic cells in the
hippocampus.

Conclusion:

The results show some efficacy of apigenin in the treatment of AD models. However,
further clinical studies are needed to confirm the clinical efficacy of apigenin
Exploring the Relationship Between Smartphone GPS Patterns and Quality of Life in Patients With Advanced Cancer and Their Family Caregivers: Longitudinal Study
Authors: Lee Kyungmi , Azuero Andres , Engler Sally , Kumar Sidharth , Puga Frank , Wright Alexi A , Kamal Arif , Ritchie Christine S , Demiris George , Bakitas Marie A , Odom J Nicholas
Published: December 2024

Background

Patients with advanced cancer and their family caregivers often experience poor quality of life (QOL). Self-report measures are commonly used to quantify QOL of family caregivers but may have limitations such as recall bias and social desirability bias. Variables derived from passively obtained smartphone GPS data are a novel approach to measuring QOL that may overcome these limitations and enable detection of early signs of mental and physical health (PH) deterioration.

Objective

This study explored the feasibility of a digital phenotyping approach by assessing participant adherence and examining correlations between smartphone GPS data and QOL levels among family caregivers and patients with advanced cancer.

Methods

This was a secondary analysis involving 7 family caregivers and 4 patients with advanced cancer that assessed correlations between GPS sensor data captured by a personally owned smartphone and QOL self-report measures over 12 weeks through linear correlation coefficients. QOL as measured by the Patient-Reported Outcomes Measurement Information System (PROMIS) Global Health 10 was collected at baseline, 6, and 12 weeks. Using a Beiwe smartphone app, GPS data were collected and processed into variables including total distance, time spent at home, transition time, and number of significant locations.

Results

The study identified relevant temporal correlations between QOL and smartphone GPS data across specific time periods. For instance, in terms of PH, associations were observed with the total distance traveled (12 and 13 wk, with r ranging 0.37 to 0.38), time spent at home (−4 to −2 wk, with r ranging from −0.41 to −0.49), and transition time (−4 to −2 wk, with r ranging −0.38 to −0.47).

Conclusions

This research offers insights into using passively obtained smartphone GPS data as a novel approach for assessing and monitoring QOL among family caregivers and patients with advanced cancer, presenting potential advantages over traditional self-report measures. The observed correlations underscore the potential of this method to detect early signs of deteriorating mental health and PH, providing opportunities for timely intervention and support.
Quantifying Patient Demand for Orthopedics Care by Region Through Google Trends Analysis: Descriptive Epidemiology Study
Authors: Qiu Abram , Meadows Kristopher , Ye Fei , Iyawe Osasu , Kenneth-Nwosa Kenneth
Published: December 2024

Background

There is a growing gap between the supply of surgeons and the demand for orthopedic services in the United States.

Objective

We analyzed publicly available online data to assess the correlation between the supply of orthopedic surgeons and patient demand across the United States. The geographic trends of this gap were assessed by using the relative demand index (RDI) to guide precision public health interventions such as resource allocation, residency program expansion, and workforce planning to specific regions.

Methods

The data used were from the US Census Bureau, Association of American Medical Colleges (AAMC) through their 2024 Electronic Residency Application Service (ERAS) directory, AAMC State Physician Workforce Data Report, and Google Trends. We calculated the normalized relative search volume (RSV) and the RDI and compared them to the densities of orthopedic surgeons across the United States. We examined the disparities with the Spearman rank correlation coefficient.

Results

The supply of orthopedic surgeons varied greatly across the United States, with a significantly higher demand for them in southern states (P=.02). The orthopedic surgeon concentration, normalized to the highest density, was the highest in Alaska (n=100), the District of Columbia (n=96), and Wyoming (n=72); and the lowest in Texas (n=0), Arkansas (n=6), and Oklahoma (n=64). The highest RDI values were observed in Utah (n=97), Florida (n=88), and Texas (n=83), while the lowest were observed in Alaska (n=0), the District of Columbia (n=5), and New Hampshire (n=7). The 7 states of Alaska, Maine, South Dakota, Wyoming, Montana, Delaware, and Idaho lacked orthopedic surgery residencies. In 2023, New York (n=19), Michigan (n=17), Ohio (n=17), Pennsylvania (n=16), and California (n=16) had the most residency programs. Demand and supply, represented by the RDI and orthopedic surgeon concentration, respectively, were strongly correlated negatively (ρ=−0.791, P<.001). States that were in the top quartile of residency programs (≥4 residency programs) exhibited a high demand for orthopedic surgeons (ρ=.6035, P=.02).

Conclusions

This study showed that regional disparities in access to orthopedic care can be addressed by increasing orthopedic residencies. The study highlights the novel application of the RDI to mapping the regional need for orthopedics, and this map allows for better targeted resource allocation to expand orthopedic surgery training.
Toxicity on Social Media During the 2022 Mpox Public Health Emergency: Quantitative Study of Topical and Network Dynamics (Preprint)
Authors: Fan Lizhou , Li Lingyao , Hemphill Libby
Published: December 2024

BACKGROUND

Toxicity on social media, encompassing behaviors such as harassment, bullying, hate speech, and the dissemination of misinformation, has become a pressing social concern in the digital age. Its prevalence intensifies during periods of social crises and unrest, eroding a sense of safety and community. Such toxic environments can adversely impact the mental well-being of those exposed and further deepen societal divisions and polarization. The 2022 mpox outbreak, initially called “monkeypox” but later renamed to reduce stigma and address societal concerns, provides a relevant context for this issue.

OBJECTIVE

In this study, we conducted a comprehensive analysis of the toxic online discourse surrounding the 2022 mpox outbreak. We aimed to dissect its origins, characterize its nature and content, trace its dissemination patterns, and assess its broader societal implications, with the goal of providing insights that can inform strategies to mitigate such toxicity in future crises.

METHODS

We collected >1.6 million unique tweets and analyzed them with 5 dimensions: context, extent, content, speaker, and intent. Using topic modeling based on bidirectional encoder representations from transformers and social network community clustering, we delineated the toxic dynamics on Twitter.

RESULTS

By categorizing topics, we identified 5 high-level categories in the toxic online discourse on Twitter, including disease (20,281/43,521, 46.6%), health policy and health care (8400/43,521, 19.3%), homophobia (10,402/43,521, 23.9%), politics (2611/43,521, 6%), and racism (1784/43,521, 4.1%). Across these categories, users displayed negativity or controversial views on the mpox outbreak, highlighting the escalating political tensions and the weaponization of stigma during this infodemic. Through the toxicity diffusion networks of mentions (17,437 vertices with 3628 clusters), retweets (59,749 vertices with 3015 clusters), and the top users with the highest in-degree centrality, we found that retweets of toxic content were widespread, while influential users rarely engaged with or countered this toxicity through retweets.

CONCLUSIONS

Our study introduces a comprehensive workflow that combines topical and network analyses to decode emerging social issues during crises. By tracking topical dynamics, we can track the changing popularity of toxic content on the internet, providing a better understanding of societal challenges. Network dynamics highlight key social media influencers and their intentions, suggesting that engaging with these central figures in toxic discourse can improve crisis communication and guide policy making.
Leveraging Large Language Models to Improve Readability of Online Patient Education Materials: Cross-sectional Study (Preprint)
Authors: Will John , Gupta Mahin , Zaretsky Jonah , Dowlath Aliesha , Testa Paul , Feldman Jonah
Published: December 2024

BACKGROUND

Online accessible patient education materials (PEMs) are essential for patient empowerment. However, studies have shown that these materials often exceed the recommended sixth grade reading level, making them difficult for many patients to understand. Large language models (LLMs) have the potential to transform PEMs into more readable educational content.

OBJECTIVE

We sought to evaluate whether three LLMs—ChatGPT, Gemini, and Claude—can optimize the readability of PEMs to the recommended reading level without compromising accuracy.

METHODS

This cross-sectional study used 60 randomly selected PEMs available online from three websites. We prompted LLMs to simplify the reading level of online PEMs. The primary outcome was the readability of the original online PEMs compared with the LLM-simplified versions. Readability scores were calculated using four validated indices Flesch Reading Ease (FRE), Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), and Simple Measure of Gobbledygook Index (SMOGI). Accuracy and understandability were also assessed as balancing measures, with understandability measured using the Patient Education Materials Assessment Tool-Understandability (PEMAT-U).

RESULTS

The original readability scores for the American Heart Association (AHA), American Cancer Society (ACS), and American Stroke Association (ASA) websites were above the recommended sixth-grade level, with mean grade level scores of 10.7,10.0, and 9.6, respectively. After optimization by the LLMs, readability scores significantly improved across all three websites when compared to the original text. Compared to the original website, Wilcoxon Signed-Rank Test showed ChatGPT improved the readability to 7.6 from 10.1 (P<.001), Gemini improved to 6.6 (P<.001), and Claude improved to 5.6 (P<.001). Word counts were significantly reduced by all LLMs, with a decrease from a mean of 410.9 to 953.9 words to a mean range of 201.9 to 248.1 words. None of the ChatGPT LLM-simplified PEMs were inaccurate, while 3.3% of Gemini and Claude LLM-simplified PEMs were inaccurate. Baseline understandability scores, as measured by PEMAT-U, were preserved across all LLM-simplified versions.

CONCLUSIONS

This cross-sectional study demonstrates that LLMs have the potential to significantly enhance the readability of online PEMs, making them more accessible to a broader audience. However, variability in model performance and demonstrated inaccuracies underscore the need for human review of LLM output. Further study is needed to identify the most reliable and effective LLM based approaches to improve readability.
Categorization of Beef Longissimus Lumborum and Gluteus Medius Muscles Based on Metabolic Attributes Is More Informative Than Muscle pH
Authors: King D. Andy , Shackelford Steven D. , Nonneman Dan , Katz Tatum S. , Wheeler Tommy L.
Published: December 2024

Muscle metabolism is generally monitored using muscle pH. However, pH does not account for all metabolic effects on meat quality. We evaluated the effectiveness of agglomerative hierarchical clustering in creating clusters of beef longissimus and gluteus medius muscles based on metabolic traits. Beef carcasses (n = 100) were selected at grading based on longissimus thoracis pH (< 5.6, 5.60 to 5.74, 5.75 to 5.9, and > 5.9). Metabolic traits characterizing oxidative and glycolytic metabolism were measured on each muscle. A subset of longissimus lumborum muscles were placed in an in vitro glycolytic system with 2 temperature decline rates to evaluate glycolytic efficiency. Gluteus medius muscles exhibited more oxidative metabolism than longissimus lumborum muscles. Metabolic traits measured in one muscle were generally positively correlated to the same trait measured in the other muscle. Clustering of metabolic traits within each muscle produced similar dendrograms. Clustering of longissimus lumborum muscles based on metabolic traits produced 4 distinct clusters (High pH, Glycolytic, Chaperone, and Soluble). Clustering of the high pH was generally, but not totally, in agreement with classifications based on pH. The remaining longissimus lumborum clusters did not differ in pH. Similar to the longissimus lumborum clusters, the gluteus medius clusters included High pH and Glycolytic clusters and a cluster with low values for protein solubility and peroxiredoxin 2 abundance. In the in vitro system, pH decline was affected by a cluster × temperature decline rate interaction (P < 0.05). The soluble cluster had the least extensive pH decline under the fast temperature decline but had the most rapid pH decline at the slower pH decline. These results indicate that clustering muscles based on several metabolic factors was more effective than categorizing muscles based on muscle pH. Metabolic variation identified by clustering was related to differences in the glycolytic machinery that can be differentially impacted by chilling rate.
“El ascenso del Pacífico” de Raúl Zurita como texto apocalíptico y de resurrección
Authors: Espinosa-Arce Juan Pablo
Published: December 2024

La lectura del grupo de poemas “El ascenso del Pacífico” del poeta chileno Raúl Zurita tiene, en su escritura y en sus imágenes, elementos cercanos a la literatura apocalíptica y a las categorías teológicas relativas a los relatos evangélicos sobre la resurrección de Jesús. A partir de un acercamiento hermenéutico al poema, el artículo pretende relevar cómo el poeta utiliza el lenguaje apocalíptico presente en el texto bíblico para hablar de la suerte de las víctimas de la Dictadura Militar chilena (1973-1990). El uso del lenguaje apocalíptico y de la resurrección constituye un espacio de imaginación y escritura, a través de la cual se genera una forma sugerente de mostrar el sentido de la historia de los grupos marginados, políticos, sociales y culturales, sentido presente tanto en el texto bíblico como en la literatura de Raúl Zurita.