bluuebunny/crossref_metadata_2025_split huggingface dataset:
Emerging Applications of Plant Antimicrobials in the Food Industry
Authors: Grover Parul , Bhardwaj Monika , Malhotra Anjleena , Sharma Ram , Pathak Anuj , Genovese Carlo , Kumar Sandeep , Rohilla Suman 
Published: June 2024

Abstract:
Food safety is a global concern with significant public health implications. Improper
food handling can harbor a wide range of pathogenic organisms. Antimicrobial agents are crucial for controlling microbes and ensuring food safety and human health. The growing demand
for natural, safe, and sustainable food preservation methods has driven research into using plant
antimicrobials as alternatives to synthetic preservatives. The food industry is now exploring innovative approaches that combine various physical methods with multiple natural antimicrobials. This review aims to outline the evolving applications of plant antimicrobials in the food
industry. It discusses strategies for managing bacteria and categorizes different plant antimicrobials, providing insights into their mechanisms of action and structures. This review offers a
comprehensive overview of antimicrobial peptides (AMPs), detailing their structural characteristics, mechanisms of action, various types, and applications in food packaging fabrication and
explaining how they contribute to food preservation. It highlights the synergistic and additive
benefits of plant antimicrobials and their successful integration with food technologies like nanotechnology, which enhances the hurdle effect, improving food safety and extending shelf life.
The review also emphasizes the importance of antimicrobial peptides and the need for further
research in this area. Safety assessment and regulatory considerations are discussed as well. By
addressing these gaps, plant antimicrobials have the potential to pave the way for more effective,
safe, and sustainable food preservation strategies in the future.
Multidimensional Uses of Bitter Melon (Momordica charantia L.)
Considering the Important Functions of its Chemical Components
Authors: Shahrajabian Mohamad Hesam , Sun Wenli 
Published: June 2024

Abstract:
Bitter melon (Momordica charantia L.) is a member of the Cucurbitaceae, which
is also known as bitter squash, bitter gourd, karela, Goya melon and balsam pear. It is a rich
source of different vitamins, potassium, zinc and other nutrients.
The main pharmaceutical benefits of bitter melon are “antiinflammatory”, “antioxidant activity”, “antimicrobial characteristic”, “anticancer activity”, and “antihelmintic activity”,
“antidiabetic effects”, “antiinflammation activity” and “treat skin conditions”. Its fruit is the
main part of the plant which has been used for medicinal and food purposes.
The primary metabolites in bitter gourd are common sugars, chlorophyll and proteins while
secondary metabolites are carotenoids, alkaloids, phenolics, curcubitane triterpenoids, saponins, etc.
The present review aims to study and survey on the nearly up-to-date results and findings
regarding the pharmaceutical advantages and health benefits of bitter melon in an organic
life.
Synthesis and In-silico Docking Analysis of 2-Phenylindolizine Acetamide
Scaffolds: Potent Anticancer and Antibacterial Ta
Authors: Shaik Adamshafi , Prasad M.S.N.A. , Prasad Puthalapattu Reddy , Kishore Pilli Veera Venkata Nanda 
Published: June 2024

Background:
Although several specialized methods for treating various cancers have
been developed, clinical outcomes for patients with colon cancer have shown unfavorable effects
in addition to chemotherapeutic drug resistance. Therefore, finding medicinal substances with
minimal to no side effects is crucial in the fight against cancer. Further, this study underscores the
significance of 2-phenylindolizine acetamide due to the diverse biological features and the extensive application of this moiety as an effective anticancer and antibacterial drug.

Objective:
The goal of the study was to investigate novel, prospective 2-phenylindolizines as anticancer and antibacterial drugs.

Methods:
The synthesized compounds were characterized by different spectral analyses like 1
H
NMR, 13C NMR, IR, and mass spectrometry. Pharmacokinetics in-silico studies were performed
to predict the drug performance in the body using the SwissADME kit. The molecular docking
was performed to check the catalytic binding site between the Topoisomerase-IV from S. pneumoniae and synthesized compounds. The antibacterial activity was assessed using the agar well
diffusion method, and the compounds were screened for in vitro anticancer activity using an MTT
assay with doxorubicin as a reference.

Results:
Interestingly, 2-phenylindolizine scaffolds 7c, 7f, and 7g revealed a remarkable antibacterial activity against relevant organisms S. aureus, E. coli, S. pneumoniae, and P. aeruginosa.
The target compounds 7e and 7h showed excellent anticancer activity against Colo-205 and
MDA-MB-231 cell lines with IC50 values of 68.62, 62.91, 54.23, and 46.34 µM, respectively.
Compounds 7a, 7f, and 7c exhibited the highest hydrogen bonding amino acid interactions Asp83
(2.23 Å), Asp83 (2.08 Å), His74 (2.05 Å), His76 (1.71 Å), and Ser80 (1.05 Å) with active site of
Topoisomerase-IV from S. pneumoniae (4KPE).

Conclusion:
This research focused on recent advances in drug design and development, as well
as Phenylindolizine derivatives and how they work on anticancer and antibacterial sites of action.
Detection of DNA N6-Methyladenine Modification through SMRT-seq
Features and Machine Learning Model
Authors: Guo Yichu , Zhang Yixuan , Liu Xiaoqing , He Pingan , Zeng Yuni , Dai Qi 
Published: June 2024

Introduction:
N6-methyldeoxyadenine (6mA) is the most prevalent DNA modification
in both prokaryotes and eukaryotes. While single-molecule real-time sequencing (SMRT-seq) can
detect 6mA events at the individual nucleotide level, its practical application is hindered by a high
rate of false positives.

Methods:
We propose a computational model for identifying DNA 6mA that incorporates comprehensive site features from SMRT-seq and employs machine learning classifiers.

Results:
The results demonstrate that 99.54% and 96.55% of the identified DNA 6mA instances in
C.reinhardtii correspond with motifs and peak regions identified by methylated DNA immunoprecipitation sequencing (MeDIP-seq), respectively. Compared to SMRT-seq, the proportion of predicted DNA 6mA instances within MeDIP-seq peak regions increases by 2% to 70% across the six
bacterial strains

Conclusion:
Our proposed method effectively reduces the false-positive rate in DNA 6mA prediction.
A Glimpse of Research Trends and Frontiers in the Etiology of Premature Ovarian Insufficiency via Bibliometric Analysis
Authors: Li Duan , Liu Yingxue , Hui Yameng , Li Bing , Hao Cuifang 
Published: June 2024

Introduction:
Premature Ovarian Insufficiency (POI) is the most common reproductive
aging disorder in women of reproductive age, which is characterized by decreased ovarian
function in women before the age of 40. Etiology research of POI has garnered interest and attention
from scholars worldwide over the past decades.

Methods:
However, to the best of our knowledge, no comprehensive survey with bibliometric
analysis has been conducted yet on the research trends of POI etiology. This article aimed to
analyze current scientific findings on the etiology of POI, offering innovative ideas for further
research. Research articles on the etiology of POI from 1994 to 2023 were collected from the
Web of Science Core Collection. A total of 456 research articles were included, and the total
number of publications increased annually. We used VOSviewer and bibliometric.com to analyze
the keywords, terms, institution, publication country/region, author name, publication journal,
and the sum of times the articles have been cited.

Results:
This study has shown that a research hotspot is the genetic etiology of POI; however,
there is still a lack of research on the impact of epigenetic alterations, iatrogenic injuries, environmental
pollution, social stress, and unhealthy lifestyles on the pathogenesis of POI.

Conclusion:
The factors illustrated here represent potential future directions for POI etiology
research and warrant more attention from researchers.
Autoimmunity in Type 2 Diabetes: When the Only Effective Therapy
Becomes Immunosuppressive
Authors: Leo Maria Laura , Locantore Pietro , Policola Caterina , Michetti Alessio , Corsello Andrea , Paoli Lorenzo Lucaccini , Pitocco Dario , Pontecorvi Alfredo 
Published: June 2024

Background::
Type B insulin resistance syndrome is a rare form of diabetes due to
the presence of anti-insulin receptor antibodies [1, 2], which causes glycemic decompensation
and antidiabetic therapy failure and instead responds to immunosuppressive therapy.

Case Report::
A 67-year-old patient was admitted to the hospital due to autoimmune hemolytic
anemia and glycemic decompensation. We first prescribed subcutaneous basal-bolus insulin and
then intravenous insulin without improvement in blood sugar levels (between 300 and 500
mg/dL). Considering the non-response to therapy and the autoimmune diathesis of the patient
(hemolytic anemia and mixed connective tissue disease), we suspected an autoimmune etiopathogenesis
of glycemic decompensation; we excluded type 1 diabetes mellitus (specific antibodies
were negative), and we considered the anti-insulin-antibodies-(-assayed and negative)
and anti-insulin receptor antibodies (not assayed due to the lack of a center specialized in this
assay in the area). Therefore, we decided to start Rituximab. After 2 weeks from the infusion,
the patient improved glycemic compensation, reducing insulin requirement. Further, 2 months
after the first infusion, the patient stopped insulin, returning to oral therapy with Metformin. To
date, the patient has completed 3 cycles of Rituximab with the benefit of glycemic control
(HbA1c 6.7%).

Conclusion::
The brilliant response to Rituximab supports the hypothesis of an autoimmune
pathogenesis. The anti-insulin receptor antibodies (in the type B insulin resistance syndrome)
affect mostly middle-aged adults, especially women, in the context of other autoimmune diseases.
Hence, it is necessary to consider the diagnosis of this rare disease in order to perform timely
and effective treatment.
Comparison of 24-hour monitoring and one-time measurement arterial stiffness in patients with bronchial asthma
Authors: Karoli N. A. , Zarmanbetova O. T. 
Published: June 2024

Objective: to compare of 24-hour monitoring and one-time measurement arterial stiffness in patients with bronchial asthma.Materials and methods: Tthe study included 100 patients with bronchial asthma. All patients underwent 24-hour monitoring and one-time measurement arterial stiffness parameters. At the same time vascular stiffness indicators were examined: pulse wave velocity in the aorta, augmentation index, arterial stiffness index.Results: Iin the patients with asthma were found to increased 24-hour arterial stiffness. Results of 24-hour monitoring arterial stiffness more than one-time measurement.Conclusions: 24-hour monitoring arterial stiffness more informative than one-time measurement.
Detection of Mild Cognitive Impairment Through Hand Motor Function Under Digital Cognitive Test: Mixed Methods Study
Authors: Li Aoyu , Li Jingwen , Chai Jiali , Wu Wei , Chaudhary Suamn , Zhao Juanjuan , Qiang Yan 
Published: June 2024

Background
Early detection of cognitive impairment or dementia is essential to reduce the incidence of severe neurodegenerative diseases. However, currently available diagnostic tools for detecting mild cognitive impairment (MCI) or dementia are time-consuming, expensive, or not widely accessible. Hence, exploring more effective methods to assist clinicians in detecting MCI is necessary.

Objective
In this study, we aimed to explore the feasibility and efficiency of assessing MCI through movement kinetics under tablet-based “drawing and dragging” tasks.

Methods
We iteratively designed “drawing and dragging” tasks by conducting symposiums, programming, and interviews with stakeholders (neurologists, nurses, engineers, patients with MCI, healthy older adults, and caregivers). Subsequently, stroke patterns and movement kinetics were evaluated in healthy control and MCI groups by comparing 5 categories of features related to hand motor function (ie, time, stroke, frequency, score, and sequence). Finally, user experience with the overall cognitive screening system was investigated using structured questionnaires and unstructured interviews, and their suggestions were recorded.

Results
The “drawing and dragging” tasks can detect MCI effectively, with an average accuracy of 85% (SD 2%). Using statistical comparison of movement kinetics, we discovered that the time- and score-based features are the most effective among all the features. Specifically, compared with the healthy control group, the MCI group showed a significant increase in the time they took for the hand to switch from one stroke to the next, with longer drawing times, slow dragging, and lower scores. In addition, patients with MCI had poorer decision-making strategies and visual perception of drawing sequence features, as evidenced by adding auxiliary information and losing more local details in the drawing. Feedback from user experience indicates that our system is user-friendly and facilitates screening for deficits in self-perception.

Conclusions
The tablet-based MCI detection system quantitatively assesses hand motor function in older adults and further elucidates the cognitive and behavioral decline phenomenon in patients with MCI. This innovative approach serves to identify and measure digital biomarkers associated with MCI or Alzheimer dementia, enabling the monitoring of changes in patients’ executive function and visual perceptual abilities as the disease advances.
Combining Clinical-Radiomics Features With Machine Learning Methods for Building Models to Predict Postoperative Recurrence in Patients With Chronic Subdural Hematoma: Retrospective Cohort Study
Authors: Fang Cheng , Ji Xiao , Pan Yifeng , Xie Guanchao , Zhang Hongsheng , Li Sai , Wan Jinghai 
Published: June 2024

Background
Chronic subdural hematoma (CSDH) represents a prevalent medical condition, posing substantial challenges in postoperative management due to risks of recurrence. Such recurrences not only cause physical suffering to the patient but also add to the financial burden on the family and the health care system. Currently, prognosis determination largely depends on clinician expertise, revealing a dearth of precise prediction models in clinical settings.

Objective
This study aims to use machine learning (ML) techniques for the construction of predictive models to assess the likelihood of CSDH recurrence after surgery, which leads to greater benefits for patients and the health care system.

Methods
Data from 133 patients were amassed and partitioned into a training set (n=93) and a test set (n=40). Radiomics features were extracted from preoperative cranial computed tomography scans using 3D Slicer software. These features, in conjunction with clinical data and composite clinical-radiomics features, served as input variables for model development. Four distinct ML algorithms were used to build predictive models, and their performance was rigorously evaluated via accuracy, area under the curve (AUC), and recall metrics. The optimal model was identified, followed by recursive feature elimination for feature selection, leading to enhanced predictive efficacy. External validation was conducted using data sets from additional health care facilities.

Results
Following rigorous experimental analysis, the support vector machine model, predicated on clinical-radiomics features, emerged as the most efficacious for predicting postoperative recurrence in patients with CSDH. Subsequent to feature selection, key variables exerting significant impact on the model were incorporated as the input set, thereby augmenting its predictive accuracy. The model demonstrated robust performance, with metrics including accuracy of 92.72%, AUC of 91.34%, and recall of 93.16%. External validation further substantiated its effectiveness, yielding an accuracy of 90.32%, AUC of 91.32%, and recall of 88.37%, affirming its clinical applicability.

Conclusions
This study substantiates the feasibility and clinical relevance of an ML-based predictive model, using clinical-radiomics features, for relatively accurate prognostication of postoperative recurrence in patients with CSDH. If the model is integrated into clinical practice, it will be of great significance in enhancing the quality and efficiency of clinical decision-making processes, which can improve the accuracy of diagnosis and treatment, reduce unnecessary tests and surgeries, and reduce the waste of medical resources.
Visual Function and Driving Performance Under Different Lighting Conditions in Older Drivers: Preliminary Results From an Observational Study
Authors: Yang Jingzhen , Alshaikh Enas , Yu Deyue , Kerwin Thomas , Rundus Christopher , Zhang Fangda , Wrabel Cameron G , Perry Landon , Lu Zhong-Lin 
Published: June 2024

Background
Age-related vision changes significantly contribute to fatal crashes at night among older drivers. However, the effects of lighting conditions on age-related vision changes and associated driving performance remain unclear.

Objective
This pilot study examined the associations between visual function and driving performance assessed by a high-fidelity driving simulator among drivers 60 and older across 3 lighting conditions: daytime (photopic), nighttime (mesopic), and nighttime with glare.

Methods
Active drivers aged 60 years or older participated in visual function assessments and simulated driving on a high-fidelity driving simulator. Visual acuity (VA), contrast sensitivity function (CSF), and visual field map (VFM) were measured using quantitative VA, quantitative CSF, and quantitative VFM procedures under photopic and mesopic conditions. VA and CSF were also obtained in the presence of glare in the mesopic condition. Two summary metrics, the area under the log CSF (AULCSF) and volume under the surface of VFM (VUSVFM), quantified CSF and VFM. Driving performance measures (average speed, SD of speed [SDspeed], SD of lane position (SDLP), and reaction time) were assessed under daytime, nighttime, and nighttime with glare conditions. Pearson correlations determined the associations between visual function and driving performance across the 3 lighting conditions.

Results
Of the 20 drivers included, the average age was 70.3 years; 55% were male. Poor photopic VA was significantly correlated with greater SDspeed (r=0.26; P<.001) and greater SDLP (r=0.31; P<.001). Poor photopic AULCSF was correlated with greater SDLP (r=–0.22; P=.01). Poor mesopic VUSFVM was significantly correlated with slower average speed (r=–0.24; P=.007), larger SDspeed (r=–0.19; P=.04), greater SDLP (r=–0.22; P=.007), and longer reaction times (r=–0.22; P=.04) while driving at night. For functional vision in the mesopic condition with glare, poor VA was significantly correlated with longer reaction times (r=0.21; P=.046) while driving at night with glare; poor AULCSF was significantly correlated with slower speed (r=–0.32; P<.001), greater SDLP (r=–0.26; P=.001) and longer reaction times (r=–0.2; P=.04) while driving at night with glare. No other significant correlations were observed between visual function and driving performance under the same lighting conditions.

Conclusions
Visual functions differentially affect driving performance in different lighting conditions among older drivers, with more substantial impacts on driving during nighttime, especially in glare. Additional research with larger sample sizes is needed to confirm these results.
Pekerjaan, Pendapatan, Pemanfaatan Pelayanan Kesehatan Peserta Jaminan Kesehatan Nasional (Non-PBI) di Kecamatan Sario
Authors: Umboh Angelina Gloria , Kolibu Febi Kornela , Korompis Grace Esther Carolina 
Published: June 2024

Kepesertaan dari Jaminan Kesehatan Nasional (JKN) merupakan hal yang wajib dimiliki oleh seluruh masyarakat Indonesia termasuk bagi orang yang bekerja untuk mendapatkan perlindungan kesehatan bersama dengan anggota keluarganya. Beberapa hal yang memiliki hubungan dengan pemanfaatan pelayanan kesehatan yaitu pekerjaan dan pendapatan. Tujuan penelitian untuk menganalisis hubungan antara pekerjaan dan pendapatan dengan pemanfaatan pelayanan kesehatan oleh peserta Jaminan Kesehatan Nasional (Non-PBI) di Kecamatan Sario. Penelitian ini merupakan penelitian kuantitatif dengan desain analitik cross sectional. Penelitian ini dilaksanakan di Kecamatan Sario pada bulan Januari – Mei 2023. Sampel yaitu Kepala Keluarga (Suami/Istri) dengan jumlah 106 responden. Hasil dari penelitian ini diperoleh nilai p-value 0.001 untuk hubungan antara pekerjaan dengan pemanfaatan pelayanan kesehatan dan nilai p-value 0,002 untuk hubungan antara pendapatan dengan pemanfaatan pelayanan kesehatan. Kesimpulan dalam penelitian ini yaitu pekerjaan dari Peserta Jaminan Kesehatan Nasional (Non-PBI) di Kecamatan Sario yaitu Pekerja Penerima Upah (PPU) (60,4%), Pekerja Bukan Penerima Upah (PBPU) (29,2%), Bukan Pekerja (10,4%). Pendapatan Keluarga dari Peserta Jaminan Kesehatan Nasional (Non-PBI) di Kecamatan Sario memiliki pendapatan tinggi (>Rp. 3.530.000) (66,0%) dan pendapatan rendah (≤Rp. 3.530.000) (34,0%). Pemanfaatan pelayanan kesehatan oleh Peserta Jaminan Kesehatan Nasional (Non-PBI) di Kecamatan Sario yaitu memanfaatkan (76,4%) tidak memanfaatkan (23,6%). Terdapat hubungan antara pekerjaan dengan pemanfaatan pelayanan kesehatan oleh peserta Jaminan Kesehatan Nasional (Non-PBI) di Kecamatan Sario. Terdapat hubungan antara pendapatan dengan pemanfaatan pelayanan kesehatan oleh peserta Jaminan Kesehatan Nasional (Non-PBI) di Kecamatan Sario.
Pemanfaatan Penggunaan Aplikasi Mobile JKN
Authors: Kusumawati Rindha Mareta , Apriyani Apriyani , Wulandari Kartina , Suwignyo Suwignyo 
Published: June 2024

Aplikasi Mobile JKN merupakan inovasi yang dilakukan oleh BPJS Kesehatan dalam meningkatkan pelayanan kepada masyarakat dalam bidang kesehatan. Melihat dengan sudah berjalannya aplikasi ini dapat diketahui apakah penggunaan aplikasi mobile JKN sudah dimanfaatkan atau belum. Tujuan penelitian ini untuk mengetahui pemanfaatan penggunaan aplikasi mobile JKN. Tempat penelitian di Fakultas Kesehatan Masyarakat Universitas Widya Gama Mahakam Samarinda dan dilaksanakan pada bulan Desember 2022 dengan sampel penelitian berjumlah 96 orang. Metode yang digunakan adalah metode kuantitatif dengan desain cross sectional. Hasil yang didapatkan ada hubungan antara pengetahuan (chi square hitung: 9,762) dan aksesbilitas (chi square hitung: 4,641) terhadap pemanfaatan aplikasi mobile JKN. Disarankan BPJS aktif melakukan sosialisasi terhadap penggunaan aplikasi mobil JKN kepada kalangan umur produktif.