bluuebunny/crossref_metadata_2025_split huggingface dataset:
Framework Nucleic Acids: A Promising Vehicle for Small Molecular Cargos
Authors: Zhang Junjiang , Li Jiayin , Sui Lei , Li Yanjing 
Published: January 2023

Abstract:
Framework nucleic acids (FNAs), which are a series of self-assembled DNA nanostructures, are highly
versatile tools for engineering intelligent molecular delivery vehicles. Owing to their precise and controllable design
and construction, excellent programmability and functionality, as well as favorable intercalation between DNA and
small molecules, FNAs provide a promising approach for small molecule delivery. This review discusses the advantages,
applications, and current challenges of FNAs for the delivery of small molecular cargo. First, the physicochemical
and biological properties that make FNAs favorable for the transport of small molecules are introduced.
Thereafter, the classification of loaded cargos and the mechanism of combination between small molecules and
FNAs are summarized in detail, and recent research on FNA-based delivery systems and their applications are highlighted.
Finally, the challenges and prospects of FNA nanocarriers are discussed to advance their exploitation and
clinical adoption.
Design and Construction of Carboxylesterase 2c Gene Knockout Rats by
CRISPR/Cas9
Authors: Liu Jie , Shang Xuyang , Yao Bingyi , Zhang Yuanjin , Huang Shengbo , Guo Yuanqing , Wang Xin 
Published: January 2023

Background:
Carboxylesterase 2 (CES2) is mainly distributed in the human liver and gut, and plays an
active role in the metabolic activation of many prodrugs and lipid metabolism. Although CES2 is of great significance,
there are still few animal models related to CES2.

Objectives:
This research aims to construct Ces2c gene knockout (KO) rats and further study the function of CES2.

Methods:
CRISPR/Cas9 gene editing technology was used to target and cleave the rat Ces2c gene. Compensatory
effects of major CES subtypes both in the liver and small intestine of KO rats were detected at mRNA levels. Meanwhile,
diltiazem and aspirin were used as substrates to test the metabolic capacity of Ces2c in KO rats.

Results:
This Ces2c KO rat model showed normal growth and breeding without off-target effects. The metabolic
function of Ces2c KO rats was verified by the metabolic study of CES2 substrates in vitro. The results showed that
the metabolic capacity of diltiazem in KO rats was weakened, while the metabolic ability of aspirin did not change
significantly. In addition, the serum physiological indexes showed that the Ces2c deletion did not affect the liver
function of rats.

Conclusion:
The Ces2c KO rat model was successfully constructed by CRISPR/Cas9 system. This rat model can not
only be used as an important tool to study the drug metabolism mediated by CES2, but also as an important animal
model to study the physiological function of CES2.
Chalcones As Potent Agents Against Staphylococcus aureus: A Computational Approach
Authors: da Cunha Xavier Jayze , dos Santos Hélcio Silva , Machado Marinho Márcia , Nunes da Rocha Matheus , Rodrigues Teixeira Alexandre Magno , Coutinho Henrique Douglas Melo , Marinho Emmanuel Silva , Sucheta  , Kumar Nitin , Mishra Raghav 
Published: January 2023

Background:
Studies on natural or synthetic products from chalcones have shown to be very
promising due to their peculiar structure that allows different possibilities of reallocation that will define
their diverse bioactivities in the creation of new substances. This creation is facilitated by the synthesis of
substances in conjunction with the molecular study, which allows a considerable advancement in
research, reducing the number of in vitro tests.

Objective:
This work focuses on the antibacterial activity of chalcones against Staphylococcus aureus,
which causes several diseases and is one of the main microorganisms with increasing resistance to
conventional drugs.

Methods:
Articles that studied antibacterial activity, efflux pump, or molecular docking were considered
complimentary analyses of chalcones. From these data, molecular docking and ADMET of the first five
chalcones were performed with the best activity found.

Results:
The result of the antimicrobial activity against Staphylococcus aureus was confirmed through in
silico study and pharmacokinetic data.

Conclusion:
The results of the antimicrobial activity of the most active chalcones against Staphylococcus
aureus were supported by in silico and pharmacokinetic studies, which not only confirmed their potential
to act against resistant strains but also established the future utility of chalcones as lead molecules or prototypes
for the synthesis of potent new antimicrobial agents against Staphylococcus aureus.
Development and Application of Chitosan Nanogel as a Sorbent for
Methotrexate Extraction
Authors: Mehdi-Alamdarlou Sanaz , Azadi Amir , Karbasian Mojgan , Ashrafi Hajar 
Published: January 2023

Background:
Successful drug extraction from biological fluids is challenging due to
interfering factors and the trace levels of the target analytes.

background:
Today, successful drug extraction, especially in biological fluids, due to interfering factors and the trace level of targeted analytics, faces many challenges in a complex matrix. Therefore, providing a suitable and sensitive method that can be increased the efficacy of extraction method has been considered.

Objective:
We aimed to provide a suitable and sensitive method to increase the efficiency of
methotrexate extraction from biological fluids.

objective:
In this study, the chitosan-based nanogels, which were synthesized by the ionic gelation method were used for solid-phase microextraction of methotrexate

Methods:
Chitosan-based nanogel synthesized by the ionic gelation method was used for solidphase
microextraction of methotrexate from aqueous and plasma media. The nanogel was characterized
in vitro in terms of morphological characteristics (transmission electron microscopy), particle
size, polydispersity index (PDI), and zeta potential. High-performance liquid chromatography
was used to determine the methotrexate concentrations and extraction yield.

method:
The in vitro characterization of nanogels were performed by evaluation of morphological characteristics with TEM, determination of particle size, PDI, and zeta potential and also the HPLC assay method for determination of methotrexate concentration in different media and determination of extraction yield

Results:
A nanogel with an average size of 135.6 nm and PDI of 0.14 was used for the nanoextraction
of methotrexate in this study. The methotrexate concentration, nanogel amount, and extraction
time affected the extraction yield. Maximum recovery percentages of 75% and 69% were
achieved when extracting methotrexate from aqueous and plasma media, respectively.

result:
The nanogels with the average size of 135.6 nm and PDI of 0.14 were used for nanoextraction of methotrexate in this study. The concentration of methotrexate, amount of nanogel, and time of extraction were evaluated as an essential factor affecting the extraction yield.

Conclusion:
The high extraction yields in both studied media indicate that using chitosan-based
nanogel as a sorbent during the solid-phase microextraction of methotrexate is a promising prospect,
with potential uses for other drugs in different media. It seems that using nanogels can be
an excellent way to improve the extraction of drugs from biological environments.

other:
non
A Review on Advanced Nanomaterials for Antibacterial Applications
Authors: Jose Jephin K. , Cherian Christie Thomas , Balachandran Manoj 
Published: January 2023

Abstract:
The management of infectious diseases is one of the major public health challenges of
the 21st century. Mutation of the microbes, biofilm formation, and other structural-morphological
behaviors have resulted in pathogens acquiring multi-drug resistance. The development of
advanced materials that can provide long-lasting and effective protection against harmful microbes
is becoming a need of the hour. Biocompatibility, efficient microbial inactivation, thermal
and chemical stability of nanomaterials help to reduce the excessive use of antibiotics and,
thus, to overcome antimicrobial resistance. Metal and metal oxide nanostructures, graphene,
carbon dots, and other two-dimensional materials exhibit excellent antimicrobial properties. This
review provides a comprehensive overview of antibacterial mechanisms and factors that help to
inactivate the bacteria by nanomaterials. It also points out the enhanced antibacterial behaviors
of the modified nanomaterials for future research concerns.

Abstract:
The management of infectious diseases is one of the major public health challenges of
the 21st century. Mutation of the microbes, biofilm formation, and other structural-morphological
behaviors have resulted in pathogens acquiring multi-drug resistance. The development of
advanced materials that can provide long-lasting and effective protection against harmful microbes
is becoming a need of the hour. Biocompatibility, efficient microbial inactivation, thermal
and chemical stability of nanomaterials help to reduce the excessive use of antibiotics and,
thus, to overcome antimicrobial resistance. Metal and metal oxide nanostructures, graphene,
carbon dots, and other two-dimensional materials exhibit excellent antimicrobial properties. This
review provides a comprehensive overview of antibacterial mechanisms and factors that help to
inactivate the bacteria by nanomaterials. It also points out the enhanced antibacterial behaviors
of the modified nanomaterials for future research concerns.
Breast Tissue Classification Method Based on Machine Learning
Authors: Li Yong , Tang Zhiling , Zhang Liangxi 
Published: January 2023

Abstract:
Early detection and treatment of breast cancer are very necessary, and effective classification
of breast tissue is helpful for the diagnosis of breast cancer; so, a classification method named
FT_GA_GBDT is proposed. First, the correlations between the features and classification labels of
breast tissue samples were determined, and features with higher correlation were analyzed statistically
and combined by weight. Thus, feature transformation (FT) is realized. The datasets were then
enhanced by calculating the mean and root mean square of the feature attributes of each adjacent
odd- and even-row sample with both belonging to the same class. Finally, the genetic algorithm
(GA) was used to search the optimal parameters of the gradient boosting decision tree (GBDT)
model, and the optimal parameters were substituted into the GBDT to classify the breast tissue. In
addition, the K-nearest-neighbor (KNN), support-vector-machine (SVM) and GBDT methods were
also used to test the breast tissue classification. Results of 6-fold cross validation on three breast
tissue datasets showed that the average Precision, Recall, and F1 score obtained by the
FT_GA_GBDT method were better than those obtained by the KNN, SVM and GBDT methods. The
results further show that the FT algorithm and searching for the optimal hyper-parameters by the GA
were helpful in improving the performance of the breast tissue classification model, which is more obvious
when the correlations between features and classification labels are generally not high.
Experiences of Connecticut Community Health Center Patients With Telemedicine During the COVID-19 Pandemic
Authors: Delvy Roberta , Coman Emil , Sanchez Kelly , Powell Wizdom , Agresta Thomas 
Published: January 2023

Background
Since COVID-19 rapidly made telemedicine a necessity, it is clear that virtual patient visits are going to be part of the new norm in continued outpatient care as we move into a postpandemic reality. However, we are still learning about patients’ experiences and preferences amid the rapid and widespread deployment of telemedicine.

Objective
The aim of this paper is to determine patients’ satisfaction with and experiences of telehealth services during the COVID-19 pandemic among those seeking care at Community Health Center Association of Connecticut member clinics.

Methods
Data were collected using a 24-question phone survey, which asked about telehealth use, frequency of telehealth visits, and barriers experienced. Participants were eligible if they aged 18 years or older, were English speaking, and were receiving care at 1 of 3 participating Community Health Center Association of Connecticut clinics.

Results
A total of 383 participants completed the phone survey throughout July 2021. The median age grouping was between 55 and 59 years, and the majority (63%) were female. Since COVID-19, in total, 78% reported having one or more audio-only telehealth visits (from 31% before), and 53% had one or more video telehealth visit (from 13% before). Most reported being very satisfied with their visits (86%) and that they felt confident in their provider’s ability to address their needs (74%). Most did not experience technical problems or have difficulty understanding how to connect to their provider. Even among older participants (60 years and older), only 28% reported having difficulty understanding how to connect to their provider, compared with 23% of 40- to 59-year-old patients and 18% of 18- to 39-year-old patients. Moreover, 45% reported being very likely to continue using telehealth even after the pandemic. However, 52% would have liked the option of in-person visits if they had been available in the past year.

Conclusions
These results suggest that most patients find telehealth visits an appropriate and accessible means of accessing health care, though some still like the option of seeing their provider in person. Future work should compare provider and patient experiences and identify optimal means of making the encounters mutually satisfying and beneficial.

Conflicts of Interest
None declared.
Predicting Clinically Significant Weight Loss in a Multimodal Commercial Digital Weight Management Program: Machine Learning Approach
Authors: Bannor Richard , Pagoto Sherry , Cardel Michelle I , Lee Alexandra , Foster Gary D , Xu Ran 
Published: January 2023

Background
Generally, adherence to diet and physical activity in behavioral weight management programs predicts weight loss. Moreover, meeting attendance and platform or app use predict weight loss. Many weight-loss–prediction studies primarily use regression. However, little is known about the levels of adherence to specific web-based weight management program tools as well as behavioral and psychosocial variables that predict significant weight loss using machine-learning approaches.

Objective
In this paper, we aimed to examine variables that can predict clinically significant weight loss (≥5%) in a multimodal commercial digital weight management program, including levels of adherence to intervention tools and changes in behavioral and psychosocial states using machine-learning approaches.

Methods
We performed secondary analyses using data from a one-arm trial online WW (formerly Weight Watchers) weight-loss intervention program, lasting 6 months, that recruited US adults with a BMI range of 25-45 kg/m2. We used a WW Bluetooth scale and digital intervention tools such as mobile app for point tracking, weekly virtual workshops, weekly wellness check-ins and a Facebook group, and changes in psychosocial and behavioral variables (ie, food craving, Pittsburgh sleep quality index, diet, hunger, and physical activity). Using the receiver operating characteristics (ROC) curve, we identified the predictors of significant weight loss, as well as the associated cut points (CP) and area-under-curve (AUC) values for each variable. We further used a classification tree to confirm the importance of these predictors and assessed the out-of-sample prediction accuracies using 5-fold cross-validation.

Results
Participants (N=153) were 70% female and 66% White, with a mean age of 41.09 (SD 13.78) years, and had a mean BMI of 31.8 kg/m2 (SD 5.0). Approximately 51% of participants lost ≥5% weight. Using ROC curve, food tracking (CP≥9.4%; AUC=0.744; P<.001), increase in self-weighing (CP≥0.5; AUC=0.733; P<.001), wellness check-in attendance (CP≥31.3%; AUC=0.723; P<.001), and workshop attendance (CP≥35.4%; AUC=0.699; P<.001) were identified as significant predictors of achieving ≥5% weight loss. We confirmed the importance of these variables using classification tree, and together they achieved an out-of-sample prediction accuracy of 78%.

Conclusions
ROC curve and classification tree provided consistent results of predictors of clinically significant weight loss (ie, food tracking, increase in self-weighing, workshop attendance, and wellness check-in attendance). Our study extends the weight management literature by using machine-learning approaches to identify significant weight loss predictors and specific levels of these predictors needed to achieve clinically significant weight loss.

Conflicts of Interest
None declared.
Physicians’ and Individuals’ Attitudes Toward Digital Mental Health Tools: Protocol for a Web Survey Study With Physician and Stakeholder Interviews
Authors: Nogueira-Leite Diogo , Cruz-Correia Ricardo 
Published: January 2023

Background
Digital transformation is impacting health care delivery and showing great market dynamism, bringing opportunities and concerns alike. Digital health applications are a vibrant segment where regulation is emerging, with Germany paving the way with its DiGA program. Simultaneously, anxiety and depression constitute global health concerns, and their prevalence is expected to worsen due to the COVID-19 pandemic and its containment measures. Portugal and its National Health System may be a useful testbed for digital health interventions seeking to manage anxiety and depression. This research methodology is very relevant in studies on mental health, making the protocol highly reusable.

Objective
The paper outlines the protocol for a research project on the attitudes of physicians and potential users toward digital mental health apps to improve access to care and patient outcomes and to reduce the burden of disease for anxiety and depression.

Methods
Web surveys will be conducted to acquire data from main stakeholders (physicians and academic community). Data analysis will replicate studies from Dahlhausen and Borghouts to derive conclusions regarding the relative acceptance and likelihood of successful implementation of digital mental health apps in Portugal.

Results
The findings of the proposed studies will elicit important information on how physicians and individuals perceive digital mental health apps interventions to improve access to care and patient outcomes and to reduce the burden of disease for anxiety and depression.

Conclusions
The results of the studies projected in this research protocol will have implications for researchers and academia, industry, and policy makers regarding the adoption and implementation of digital health mental apps and associated interventions.

Conflicts of Interest
None declared.
Effect of Mini-Dose Ready-to-Use Liquid Glucagon on Preventing Exercise-Associated Hypoglycemia in Adults With Type 1 Diabetes
Authors: Aronson Ronnie , Riddell Michael C. , Conoscenti Valentina , Junaidi M. Khaled 
Published: January 2023

OBJECTIVETo determine effect of mini-dose, ready-to-use glucagon on incidence of exercise-associated hypoglycemia (EAH) in adults with type 1 diabetes.RESEARCH DESIGN AND METHODSIndividuals initially participated in the in-clinic training phase for which they were randomly assigned to a crossover design: 150 µg glucagon (treatment arm A) or placebo (arm B) subcutaneously, immediately before exercise, plus 50% reduction in continuous subcutaneous insulin infusion (CSII) basal delivery rate. Completers were then rerandomly assigned in the 12-week outpatient investigational phase: arm A, B, or open-label C, 150 µg glucagon alone. Participants were to undertake their usual aerobic exercise at moderate to high intensity for 30 to 75 min in real-world settings. Data were analyzed for incidence of level 1 hypoglycemia based on self-monitoring blood glucose and for various secondary and exploratory end points.RESULTSOf 48 participants who completed the training phase, 45 continued to the outpatient phase. For all exercise sessions in the outpatient phase (n = 795), incidence of level 1 hypoglycemia was lower in both glucagon arms (A, 12% [P &lt; 0.0001]; C, 16% [P = 0.0032]) than in the placebo arm (B, 39%). Times below range, in range, and above range from 0 to 300 min did not significantly differ among treatment arms. Consumed grams of exercise carbohydrates were lower with glucagon use than with placebo use but did not reach statistical significance (P = 0.12). Adverse events were similar among treatment arms.CONCLUSIONSMini-dose glucagon with or without 50% reduction in CSII basal delivery rate may help to decrease EAH incidence in adults with type 1 diabetes.
Impact of Advanced Hybrid Closed Loop on Youth With High-Risk Type 1 Diabetes Using Multiple Daily Injections
Authors: Boucsein Alisa , Watson Antony S. , Frewen Carla M. , Sanders Olivia J. , Haszard Jillian J. , Jones Shirley D. , Milford-Hughes Philippa J. , de Bock Martin I. , Wheeler Benjamin J. 
Published: January 2023

OBJECTIVETo evaluate glycemic outcomes in youth (aged 13–25 years) with type 1 diabetes and high-risk glycemic control (HbA1c ≥8.5% [69 mmol/mol]) on multiple daily injection (MDI) therapy after transitioning to advanced hybrid closed loop (AHCL) therapy.RESEARCH DESIGN AND METHODSThis prospective, 3-month, single-arm, dual-center study enrolled 20 participants, and all completed the study.RESULTSHbA1c decreased from 10.5 ± 2.1% (91.2 ± 22.8 mmol/mol) at baseline to 7.6 ± 1.1% (59.7 ± 11.9 mmol/mol), and time spent in target range 70–180 mg/dL (3.9–10.0 mmol/L) increased from 27.6 ± 13.2% at baseline to 66.5 ± 9.8% after 3 months of AHCL. Two episodes of diabetic ketoacidosis attributed to infusion set failure occurred.CONCLUSIONSAHCL has the potential to improve suboptimal glycemia in youth with type 1 diabetes previously on MDI therapy.
Impact of Advanced Hybrid Closed-Loop on Youth with High-Risk Type 1 Diabetes Using Multiple Daily Injections
Authors: Boucsein Alisa , Watson Antony S. , Frewen Carla M , Sanders Olivia J. , Haszard Jillian J. , Jones Shirley D. , Milford-Hughes Philippa J. , Bock Martin I. de , Wheeler Benjamin J. 
Published: January 2023

<p> </p>
<p>Objective: To evaluate glycemic outcomes in youth (aged 13–25 years) with type 1 diabetes and high-risk glycemic control [HbA1c ≥ 8.5% (69 mmol/mol)] on multiple daily injections (MDI) after transitioning to advanced hybrid closed-loop (AHCL) therapy. </p>
<p>Research Design and Methods: This prospective 3-month single-arm, dual-center study enrolled 20 participants, and all completed the study. </p>
<p>Results: HbA1c decreased from 10.5 ± 2.1% (91.2 ± 22.8 mmol/mol) at baseline to 7.6 ± 1.1% (59.7 ± 11.9 mmol/mol) and time spent in target range 70–180 mg/dL (3.9–10.0 mmol/L) increased from 27.6 ± 13.2% at baseline, to 66.5 ± 9.8% after 3 months of AHCL. Two episodes of diabetic ketoacidosis attributed to infusion set failure occurred. </p>
<p>Conclusions: AHCL has the potential to improve suboptimal glycemia in youth with type 1 diabetes previously on MDI.</p>