bluuebunny/crossref_metadata_2025_split huggingface dataset:
In-Vitro and In-Silico Studies of Brevifoliol Ester Analogues against Insulin Resistance Condition
Authors: Binwal Monika , Sen Sumati , Vishwakarma Sadhna , Sarfraz Aqib , Bhukya Balakishan , Khan Feroz , Negi Arvind Singh , Srivastava Santosh Kumar , Bawankule Dnyaneshwar U. 
Published: September 2024

Background:
Brevifoliol is a diterpenoid that occurs naturally in the plants of Taxus
genus and is widely used as chemotherapy agent for the management of cancer. A series of semisynthetic
esters analogues of brevifoliol were prepared by Steglich esterification and attempted
for their pharmacological potential against insulin resistance conditions using in-vitro and
in-silico assays.

Objective:
The aim of this study is to understand the pharmacological potential of eighteen semisynthetic
analogs through Steglich esterification of Brevifoliol against insulin resistance condition

Methods:
In the in-vitro study, insulin resistance condition was induced in skeletal muscle cells
using TNF-α, pro-inflammatory cytokine and these cells were treated with brevifoliol analogues.
The most potent analouge was further validated using in-silico docking study against the tumor
necrosis factor (TNF-α) (PDB ID: 2AZ5) and Human Insulin Receptor (PDB ID: 1IR3), using
the Auto dock Vina v0.8 program.

Results:
Although, all the analogues of Brevifoliol significantly exhibited the pharmacological
potential. Among all, analogue 17 was most potent in reversing the TNF-α induced insulin resistance
condition in skeletal muscle cells and also to inhibit the production of TNF-α in LPSinduced
inflammation in macrophage cells in a dose-dependent manner. Similarly, in-silico molecular
docking studies revealed that analogue 17 possesses a more promising binding affinity
than the selected control drug metformin toward the TNF-α and insulin receptor.

Conclusion:
These findings suggested the suitability of analogue 17 as a drug-like candidate for
further investigation toward the management of insulin resistance conditions.
CvTMorph: Improving Local Feature Extraction in Medical Image Registration for Respiratory Motion Modeling with Convolutional Vision Transformer
Authors: Chen Peizhi , Zou Xupeng , Gou Yifan 
Published: September 2024

Background:
Accurately modeling respiratory motion in medical images is crucial for various applications, including radiation therapy planning. However,
existing registration methods often struggle to extract local features effectively, limiting their performance.

Objective:
In this paper, we aimed to propose a new framework called CvTMorph, which utilizes a Convolutional vision Transformer (CvT) and
Convolutional Neural Networks (CNN) to improve local feature extraction.

Methods:
CvTMorph integrates CvT and CNN to construct a hybrid model that combines the strengths of both approaches. Additionally, scaling and square
layers are added to enhance the registration performance. We have evaluated the performance of CvTMorph on the 4D-Lung and DIR-Lab datasets
and compared it with state-of-the-art methods to demonstrate its effectiveness.

Results:
The experimental results have demonstrated CvTMorph to outperform the existing methods in terms of accuracy and robustness for respiratory
motion modeling in 4D images. The incorporation of the convolutional vision transformer has significantly improved the registration performance
and enhanced the representation of local structures.

Conclusion:
CvTMorph offers a promising solution for accurately modeling respiratory motion in 4D medical images. The hybrid model, leveraging
convolutional vision transformer and convolutional neural networks, has proven effective in extracting local features and improving registration
performance. The results have highlighted the potential of CvTMorph for various applications, such as radiation therapy planning, and provided a
basis for further research in this field.
A Novel Electrochemiluminescence (ECL) Immunoassay for Detection of Anti-drug Antibodies (ADAs) to a Monoclonal Antibody (mAb) PYX-106
in Human Serum
Authors: Yin Feng , Adhikari Diana , Liu Xiaodong F. , Xu Tao , Liao Lili , Landauer Tessa , Ke Yan , Yang Wensheng , Simon Teri , Lei Wenji , Pinkas Jan 
Published: September 2024

Background:
PYX-106 is a fully human monoclonal antibody (mAb), targeting sialic acid
binding immunoglobulin-like lectin-15 (Siglec-15), an immunosuppressive agent with widespread
expression across various tumor types.

Methods:
To facilitate the evaluation of immunogenicity for PYX-106 in human serum, a validated
method was established to enable the detection (screening, confirmatory, and titration) of antibodies
to PYX-106 in human serum samples.

Results:
The Screening Cut-Point Factor (SCPF), Confirmatory Cut-Point (CCP), and Titration
Cut-Point Factor (TCPF) were found to be 1.51, 26.9%, and 1.86, respectively. Sensitivity was determined
to be 1.81 ng/mL in the screening assay and 1.25 ng/mL in the confirmatory assay. Low
Positive Control (LPC) was set at 6.00 ng/mL, and High Positive Control (HPC) was set at 1000
ng/mL. The drug tolerance was up to 500 μg/mL at the HPC level, up to 241 μg/mL at the ADA 100
ng/mL level, and up to 38.9 μg/mL at the LPC level. The intra-assay percent coefficient of variation
(%CV) was ≤ 2.1% for Positive Controls (PCs) in the screening assay and ≤ 1.0% for PCs in the
confirmatory assay. The inter-assay %CV was ≤ 15.3% for PCs in the screening assay and ≤ 2.1%
for PCs in the confirmatory assay. No hook effect, hemolysis effect, lipemia effect, or bilirubin interference
was found in this ADA method. Anti-PYX-106 antibodies were found stable in human serum
for at least 23 hours 51 minutes at room temperature or after six freeze/thaw cycles.

Conclusion:
Anti-PYX-106 ADA, bioanalytical assay validation, was reported for the first time in
any biological matrix. This ADA method has been successfully applied to human sample analysis to
support a clinical study.
Preparation and Photocatalytic Activity of Cerium-doped TiO2/C Composite Aerogels
Authors: Shao Xia , Li Wenjue , Zhang Zitong , Xia Lei 
Published: September 2024

Background:
In recent years, the problem of dye wastewater pollution has garnered significant
attention. To solve this problem, photocatalysis technology has emerged as a promising approach,
offering significant advantages and serving as a pivotal method for the degradation of
wastewater containing dyes. Due to the unique electrochemical properties of rare earth metal elements
and the ability of Ce doping to suppress and prohibit the recombination of the photogenerated
electron-hole pairs, a simple sol-gel method was employed for the preparation of cerium-doped
TiO2/C aerogel composites in this paper.

Methods:
When the sol-gel reaction occurs, cerium nitrate is added and the amount of cerium nitrate
is changed to prepare a set of samples. The samples are comprehensively characterized, and their
photocatalytic activity is evaluated by photocatalytic degradation of methylene blue (MB).

objective:
Due to the unique electrochemical properties of rare earth metal elements and the ability of Ce doping to suppress prohibit the recombination of the photogenerated electron-hole pairs, a simple sol-gel method was employed for the preparation of cerium-doped TiO2/C aerogel composites in this paper.

Results:
The results showed that the photocatalytic degradation rate of MB is the highest when the
cerium nitrate in TiO2 was 0.3 mmol, due to its relatively large specific surface area and moderate
TiO2 grains, as Ce content increases. The photocatalytic efficiency of MB remained relatively stable
with increasing sample dosage. When the MB concentration was 20 mg/L, the photocatalytic degradation
rate under visible light irradiation was higher than that under UV irradiation. Defects can be
formed on the surface of TiO2 through Ce doping, where more surface groups are formed, thus improving
the photocatalytic activity of the sample.

Conclusion:
This study shows that cerium-doped TiO2/C has the potential to effectively solve the
problem of dye wastewater pollution in nature.
Bacterial Culture Results and Antibiograms in Chronic Rhinosinusitis Patients in Mekong Delta, Vietnam
Authors: Viet Nguyen Trieu , Hung Nguyen Thanh , Khang Ho Anh , Lien Nguyen Thi Ngoc 
Published: September 2024

Background
The inappropriate use of antibiotics for chronic rhinosinusitis, exceeding established guidelines, is a growing concern in Vietnam. This practice is contributing to the emergence and spread of antibiotic-resistant bacteria.

Objective
This study aimed to evaluate the results of culture and antibiogram of bacteria on patients in the Mekong Delta, Vietnam, who were treated for chronic rhinosinusitis in 2022-2023.

Methods
It is a cross-sectional study, including 113 patients who came for treatment with a diagnosis of chronic rhinosinusitis at Can Tho University of Medicine and Pharmacy Hospital, Mekong Delta, Vietnam, from 2022-2023. The bacterial cultures were identified, and their antibiograms were performed in the Microbiology laboratory of Can Tho University of Medicine and Pharmacy. Statistical analysis was performed with SPSS version 20.0.

Results
The rate of bacterial growth was 97.35%. The cultured bacterial composition had 8 genera, 22 species, and 116 samples, of which 2 bacterial species were cultured, accounting for 5.45%. Antibiogram showed that Streptococcus pneumonia was resistant to cefaclor (83.33%), cefuroxime (100%), trimethoprim+
sulfamethoxazole (100%), and susceptible to chloramphenicol (100%) and vancomycin (100%). Streptococcus haemolyticus was resistant to amoxicillin+clavulanic acid (85.71%) and ampicillin (100%) and susceptible to linezolid (100%) and rifampin (100%). Streptococcus epidermidis was resistant to amoxicillin+clavulanic acid (82.35%), ampicillin (100%), penicillin (100%) and susceptible to linezolid (91.18%) and rifampin (88.24%). Streptococcus aureus was resistant to amoxicillin+clavulanic acid (95%) and ampicillin+sulbactam (90%), and susceptible to linezolid (90%) and synercid (95%). Pseudomonas aeruginosa was resistant to amikacin (100%), meropenem (100%) and tobramycin (90%).

Conclusion
This study offers some useful information to doctors in the Mekong Delta and across Vietnam. It can be used as a good orientation in choosing the most appropriate antibiotics for treating patients with chronic rhinosinusitis. It is important to be conscious of the current trend of the bacteriological profiles and to regulate the antibiotic treatment regime to improve the effectiveness and reduce antibiotic resistance rate.
In vitro Tolerance of Staphylococcus haemolyticus and Candida albicans Biofilms to Dalbavancin and Anidulafungin
Authors: Mora Francisco , Ballaz Santiago , Castillo Ivette , Ramirez Hegira , Fernandez Moreira Esteban 
Published: September 2024

Background:
Concerns about infections resulting from bacterial biofilm formation in invasive devices such as catheters and prostheses are becoming widespread in the public health domain. Staphylococcus haemolyticus, a coagulase-negative bacterium, and Candida albicans, a yeast, have become recurrent pathogens of these diseases because their presence in these devices enhances the likelihood of infection. It is believed that these microorganisms produce biofilms, which complicate treatment and slow the patient´s recuperation. Dalba-vancin is a semisynthetic, lipoglycopeptide-class antibiotic utilized as an anti-infective agent to break down gram-positive bacteria biofilms. Anidulafungin is an echinocandin class anti-fungal medication that works very well against resistant yeast strains and removes biofilms.

Objective:
This study aims to examine the anti-infective agents´ tolerance to the biofilms of Staphylococcus haemolyticus and Candida albicans.

Methods:
Polymicrobial biofilms were grown in a CDC Biofilm Reactor (CBR) for use in in vitro experiments.

Results:
When dalbavancin maintained its antibiotic activities against Staphylococcus hae-molyticus in comparison with their activity against the sessile forms, the antifungal anidula-fungin lost efficacy in eliminating Candida albicans.

Conclusion:
The planktonic forms of microbes are examined in relation to the tolerance to these anti-infective drugs.
Recent Advances in Functions and Biotechnological Potential of Pleiotropic Transcriptional Factor AdpA
Authors: Ostash Bohdan 
Published: September 2024

:
The specialized metabolism of the members of class Actinomycetes served as one of
the deepest sources of compounds for the pharmaceutical industry. Within this class species of
genus Streptomyces stand out as the most diverse and prolific producers of novel scaffolds. At
some point at the end of the 20th century, chemical-microbiological screening of actinomycetes
seemed to largely sample their specialized metabolism chemical space. Contrary to traditional discovery
methods that directly focus on the molecule or its bioactivity, the availability of sequenced
actinomycete genomes opens the door for novel biosynthetic gene clusters (BGC) for specialized
metabolism. The genome-based approaches reveal the striking richness and diversity of BGCs, to
which the “pre-genome” discovery paradigm was myopic. In most cases, small molecules encoded
within these BGCs remain unknown, and finding efficient ways to probe such unexplored
BGCs becomes one of the pressing issues of current biotechnology. Here, the focus is on the biology
of pleiotropic transcriptional factor (TF) AdpA, whose gene is invariably present in Streptomyces
genomes. The review will portray how this TF impacts the morphogenesis and metabolism
of Streptomyces and how it can be exploited to discover novel natural products.
Optimization and Characterization of Mucoadhesive Buccal Films using Mimosa Seed-based Natural Polymer for Controlled Drug Release: A Sustainable Approach
Authors: Sabale Vidya , Khade Ankita , Jiwankar Manasi , Sabale Prafulla 
Published: September 2024

Background:
The present study attempted to develop cost-effective, biocompatible and
biodegradable mucoadhesive buccal films exploring and using Mimosa pudica seed polymer,
marking its potential as a sustainable drug delivery in pharmaceutical development.

Methods:
The extracted polymer was characterized for solubility, viscosity, loss on drying, pH,
swelling index, starch, and mucilage ingredients. Compatibility between the polymer, drug, and excipients
was assessed using FTIR analysis. Buccal film trial batches were formulated using the
film casting technique and were optimized using the 23 full factorial design. Optimized formulation
was characterized for drug release, permeability, mucoadhesive study, similarity, and difference
factor along with histological and stability study.

Results:
The polymer’s pH, loss on drying, swelling index, and viscosity were 6.2, 6.8%, 81.77%,
and 50,000 cP respectively. FTIR studies showed the compatibility between natural polymer, the
model drug metoprolol succinate, and the excipients used. The early trial batches of polymeric
films showed an extended drug release comparable to the standard polymers with a good permeability
flux of 0.4048 ± 0.081 mcg*cm-2*h-1. The optimized film provided a controlled release of
52.31 ± 0.035% for more than 8 h following zero order kinetics. Mucoadhesive strength was
found to be 32.25 ± 0.29 g. The similarity factor f2 (90.2) and difference factor f1 (8.197) indicated
no significant difference compared to the standard formulation. Histological study demonstrated
the non-irritant nature of the films and stability was established from the stability studies.

Conclusion:
Thus, a sustainable approach using natural polymeric buccal films was found promising
for mucoadhesion and controlled release.
Correct Use of HCQ Did Not Cause Extra Fatalities in COVID-19 Infection
Authors: Boretti Alberto , Banik Bimal 
Published: September 2024

Abstract:
A recent study has claimed that nearly 17,000 people in the U.S. and five other countries
died prematurely during the early stages of the COVID-19 pandemic due to the compassionate use of
hydroxychloroquine (HCQ) for treatment. However, this study's conclusions are contentious due to
reliance on flawed data and the neglect of significant evidence supporting HCQ's efficacy in early
outpatient treatment. Our critique challenges the validity of the study, highlighting its methodological
weaknesses and the confounding factors it overlooked. By reviewing the comprehensive body of
scientific literature, we demonstrate that treatment with HCQ, particularly in combination with zinc
and antibiotics, consistently yielded positive outcomes, especially in the early stages. This work aims
to provide a balanced and objective assessment of HCQ's impact on COVID-19, emphasizing the
importance of early intervention and proper dosage.
Predicting Depressive Symptoms Using GPS-Based Regional Data in Germany With the CORONA HEALTH App During the COVID-19 Pandemic: Cross-Sectional Study
Authors: Edler Johanna-Sophie , Winter Michael , Steinmetz Holger , Cohrdes Caroline , Baumeister Harald , Pryss Rüdiger 
Published: September 2024

Background
Numerous studies have been conducted to predict depressive symptoms using passive smartphone data, mostly integrating the GPS signal as a measure of mobility. Environmental factors have been identified as correlated with depressive symptoms in specialized studies both before and during the pandemic.

Objective
This study combined a data-based approach using passive smartphone data to predict self-reported depressive symptoms with a wide range of GPS-based environmental factors as predictors.

Methods
The CORONA HEALTH app was developed for the purpose of data collection, and this app enabled the collection of both survey and passive data via smartphone. After obtaining informed consent, we gathered GPS signals at the time of study participation and evaluated depressive symptoms in 249 Android users with the Patient Health Questionnaire-9. The only GPS-based data collected were the participants’ location at the time of the questionnaire, which was used to assign participants to the nearest district for linking regional sociodemographic data. Data collection took place from July 2020 to February 2021, coinciding with the COVID-19 pandemic. Using GPS data, each dataset was linked to a wide variety of data on regional sociodemographic, geographic, and economic characteristics describing the respondent’s environment, which were derived from a publicly accessible database from official German statistical offices. Moreover, pandemic-specific predictors such as the current pandemic phase or the number of new regional infections were matched via GPS. For the prediction of individual depressive symptoms, we compared 3 models (ie, ridge, lasso, and elastic net regression) and evaluated the models using 10-fold cross-validation.

Results
The final elastic net regression model showed the highest explained variance (R2=0.06) and reduced the dataset from 121 to 9 variables, the 3 main predictors being current COVID-19 infections in the respective district, the number of places in nursing homes, and the proportion of fathers receiving parental benefits. The number of places in nursing homes refers to the availability of care facilities for the elderly, which may indicate regional population characteristics that influence mental health. The proportion of fathers receiving parental benefits reflects family structure and work-life balance, which could impact stress and mental well-being during the pandemic.

Conclusions
Passive data describing the environment contributed to the prediction of individual depressive symptoms and revealed regional risk and protective factors that may be of interest without their inclusion in routine assessments being costly.
Predicting and Monitoring Symptoms in Patients Diagnosed With Depression Using Smartphone Data: Observational Study
Authors: Ikäheimonen Arsi , Luong Nguyen , Baryshnikov Ilya , Darst Richard , Heikkilä Roope , Holmen Joel , Martikkala Annasofia , Riihimäki Kirsi , Saleva Outi , Isometsä Erkki , Aledavood Talayeh 
Published: September 2024

Background
Clinical diagnostic assessments and the outcome monitoring of patients with depression rely predominantly on interviews by professionals and the use of self-report questionnaires. The ubiquity of smartphones and other personal consumer devices has prompted research into the potential of data collected via these devices to serve as digital behavioral markers for indicating the presence and monitoring of the outcome of depression.

Objective
This paper explores the potential of using behavioral data collected with smartphones to detect and monitor depression symptoms in patients diagnosed with depression. Specifically, it investigates whether this data can accurately classify the presence of depression, as well as monitor the changes in depressive states over time.

Methods
In a prospective cohort study, we collected smartphone behavioral data for up to 1 year. The study consists of observations from 164 participants, including healthy controls (n=31) and patients diagnosed with various depressive disorders: major depressive disorder (MDD; n=85), MDD with comorbid borderline personality disorder (n=27), and major depressive episodes with bipolar disorder (n=21). Data were labeled based on depression severity using 9-item Patient Health Questionnaire (PHQ-9) scores. We performed statistical analysis and used supervised machine learning on the data to classify the severity of depression and observe changes in the depression state over time.

Results
Our correlation analysis revealed 32 behavioral markers associated with the changes in depressive state. Our analysis classified patients who are depressed with an accuracy of 82% (95% CI 80%-84%) and change in the presence of depression with an accuracy of 75% (95% CI 72%-76%). Notably, the most important smartphone features for classifying depression states were screen-off events, battery charge levels, communication patterns, app usage, and location data. Similarly, for predicting changes in depression state, the most important features were related to location, battery level, screen, and accelerometer data patterns.

Conclusions
The use of smartphone digital behavioral markers to supplement clinical evaluations may aid in detecting the presence and changes in severity of symptoms of depression, particularly if combined with intermittent use of self-report of symptoms.