bluuebunny/crossref_metadata_2025 huggingface dataset:
Mechanistic analysis of the association between nucleotide excision repair factors and immune infiltration in gastric cancer
Authors: Liu Ya , Chen Siai , Pang Yidan , An Juan , Su Zhanhai
Published: April 2023

Background The aim was to analyze the association between the TME and NER factors, and further to explore the effect of miRNAs on the gene expression of NER factors. This was to reveal the regulatory mechanism between DNA damage repair and the TME of GC.Methods ESTIMATE and CIBERSORT-based algorithms were used to assess the relationship between DDR scores and infiltration of immune cells in a TCGA-GC cohort. Eight databases were used to predict miRNAs regulating NER factors and qRT-PCR experiments were used to validate the regulatory relationship between key miRNAs and NER factors.Results (1) GC patients were divided into two groups with high and low DDR capacity; (2) DDR score was correlated with the proportion of immune cell infiltration; (3) DDR score was negatively correlated with FOXO3A but positively correlated with PDL1; (4) GO functional enrichment analysis between DDR subgroups was mainly about rRNA; (5) has-miR-3163, has-miR-944, has-miR-590-3P, miR-551b-5p,has-miR-548c-3p targeted many NER genes; (7) NER factors, RAD23B, POLR2A, ERCC8, CUL3, CUL5, GTF2H5, ERCC4 POLR2H, POLR2D, and GTF2H1, are predicted to be regulated by upstream miRNAs, thereby influencing the immune microenvironment and even the progression of GC.Conclusions NER factors may influence GC progression by working on the TME.
Relationship Between Surface Topography and Fatigue Life of Deep Rolled AISI 4140 Steel
Authors: Martins Augusto Moura , de Oliveira Diogo Azevedo , Magalhães Frederico De Castro , Abrão Alexandre Mendes
Published: April 2023

Deep rolling is an effective and economically viable mechanical surface treatment that induces surface deformation by the action of a rolling tool. It is capable of reducing roughness, increasing hardness and inducing compressive residual stresses on the workpiece surface, thus increasing the fatigue strength of the component. This behavior, however, is only achieved with use of suitable parameters. This work investigates the surface topography and fatigue life of hardened AISI 4140 steel subjected to turning followed by deep rolling under distinct conditions. The findings indicates that for the turned samples, roughness increases with turning feed rate, resulting in fatigue life reduction. Deep rolling promotes a roughness reduction and an increase in fatigue life and, surprisingly, the samples with the highest surface roughness after turning achieved the longest fatigue lives after deep rolling. The areal power spectral density (APSD) analysis of the surface topography was able to identify distinct effects of deep rolling for each level of pressure used. The morphology analysis indicates an initial loss of orientation, followed by the creation of a new orientation after deep rolling under high pressures. Furthermore, the interaction between deep rolling feed and deep rolling pressure presents a significant effect on the roughness parameters, indicating that different behaviors depend on each parameter level used. Deep rolling also affects the form of the fatigue fracture, reducing the number of nucleation sites, and modifying the final overload fracture site.
Expression of anoikis-related genes and potential biomarkers in colon cancer
Authors: Guo Ti , Wang Yadong
Published: April 2023

Colon cancer (CC) is a malignant tumor of the digestive tract with an increasing incidence and poor prognosis. Previous studies have shown that anovulation apoptosis plays a crucial role in the occurrence and development of cancer. However, the expression pattern of anovulatory apoptosis-related genes (ANRGs) in the CC subtype and its relationship with immunity is still unclear. This study aims to explore the role of ANRGs in the CC subtype and their relationship with the prognosis of colon cancer. Transcriptome data and clinical data of CC patients were obtained from the TCGA database and GEO database. Then, this paper divides the samples into two subtypes by consensus clustering. The prognosis of the two subtypes is significantly different.We found that multiple ANRGs played a vital role in the prognosis of colon cancer. In addition, we developed an ANRGs risk model for CC. Patients were divided into the high-risk group and the low-risk group according to the risk score. There are significant differences in survival and immune landscape between the two groups. The ANRGs related to prognosis and risk scores obtained in this paper can provide a basis for the treatment of CC.
An assessment of Individual, community and state-level factors associated with inadequate iodised salt consumption among pregnant and lactating women in Nigeria
Authors: Kareem Yusuf Olushola , Ameyaw Edward K. , Mensima Efua , Adegboye Oyelola A , Yaya Sanni
Published: April 2023

Background Iodine deficiency is the most common cause of thyroid disease, and in its severe form can result in cretinism; the impairment of the brain development of a child. Pregnant and breastfeeding women daily iodine requirement is elevated due to physiological changes in iodine metabolism, requiring up to double the iodine intake of other women. Despite the introduction of salt iodisation in many countries to control iodine deficiency disorders, adverse effects of inadequate iodine intake continue to be a problem.Methods This study utilised the Multiple Indicator Cluster Survey to assess factors associated with inadequate iodised salt consumption among pregnant women and breastfeeding mothers in Nigeria. The descriptive analysis was presented using frequencies and percentages. The prevalence of adequate and inadequate iodised salt consumption with their 95% confidence interval were computed. Several multi-level mixed effect log-binomial logistic regressions was used to explore the factors associated with inadequate iodised salt consumption. The Loglikelihood, Akaike Information Criterion and Bayesian Information Criterion were used to assess the goodness of fit of the models. All analyses were adjusted for the complex survey design and analysed using Stata 15.0 at p < 0.05.Results Our findings revealed that pregnant and breastfeeding women living in most deprived communities, with no formal education, poor wealth status, and those residing in the North West and South West region were more likely to consume salt with inadequate iodine.Conclusions There is a need to enhance women’s economic opportunities and empowerment as well as sensitisation on their nutritional requirements during pregnancy and breastfeeding.
ChatGPT for Arabic-English Translation: Evaluating the Accuracy
Authors: Khoshafah Faten
Published: April 2023

Cross-cultural communication has become more accessible with the advent of ChatGPT as a translator for multiple language pairings, including English and Arabic. However, the accuracy of ChatGPT's translation needs to be evaluated due to limitations in its understanding of domain-specific terminology and cultural context. This article aims to evaluate the translation accuracy of ChatGPT 3.5 by comparing its outputs with professional translations of various text genres, such as historical, literary, media, legal, and scientific. The results indicate that ChatGPT can be used as a translator for simple content, though it struggles with complexities that require human intervention. Despite ChatGPT generally providing accurate translations, its limitations make it unsuitable for some texts, such as legal documents, medical reports, scientific studies, and literary works. Therefore, users should be cautious while using ChatGPT for such texts, and human intervention is required to ensure accuracy. The article further offers recommendations for students and translators who frequently use ChatGPT as a translator. Overall, ChatGPT is a valuable tool for cross-cultural communication, but its limitations should be kept in mind while using it as a translator.
PCSK9 Inhibition in Atherosclerotic Cardiovascular Disease
Authors: Delialis Dimitrios , Dimopoulou Maria-Angeliki , Papaioannou Maria , Kotsira Georgia , Maneta Eleni , Mavraganis Georgios , Loutos Christos , Georgiopoulos Georgios , Stamatelopoulos Kimon
Published: April 2023

Proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i) represent a novel class of hypolipidemic
drugs, providing an additional therapeutic option over conventional hypolipidemic treatments. Given
the constantly lowering recommended LDL-C goals, low goal achievement rate and low compliance with
treatment, new hypolipidemic drug classes may substantially contribute to residual risk reduction for atherosclerotic
cardiovascular disease (ASCVD). This review aims to summarize contemporary evidence on the clinical
role of PCSK9i in ASCVD prevention. PubMed and MEDLINE databases were searched for keywords in
studies on PCSK9i and ASCVD. Approved PCSK9i are the monoclonal antibodies (Mabs), evolocumab and
alirocumab, targeting PCSK9, and inclisiran, a small interfering RNA inhibiting PSCK9 synthesis. Overall,
PCSK9i effectively reduced LDL-C and other atherogenic lipoproteins, including apolipoprotein B and lipoprotein(
a) primarily. PSCK9i Mabs improved imaging markers reflecting coronary atherosclerotic plaque vulnerability
and reduced ASCVD events in high-risk patients after short-term treatment ( < 3 years follow-up). They
are currently indicated as a third-line treatment for secondary prevention and primary prevention in patients
with familial hypercholesterolemia at high risk of not achieving their LDL-C goals. Patients with higher baseline
ASCVD risk receive greater benefits from PCSK9i. Recent evidence suggests that evolocumab was effective
and safe after long-term treatment. Ongoing trials investigate new therapeutic indications for PCSK9i while
their cost-effectiveness is still being considered. PCSK9i is a novel hypolipidemic drug class currently indicated
for reducing residual risk in secondary ASCVD prevention and high-risk patients.
Advances in the Therapeutic Potential of Inhibitors Targeting Glycogen
Synthase Kinase 3 in Inflammatory Diseases
Authors: Zhu Yifan , Wang Hui , Guo Yueyue , Cao Jie , Li Huanqiu
Published: April 2023

Glycogen synthase kinase-3 (GSK3) is one of the important serine/threonine protein kinases
and has two isoforms, namely, GSK3α and GSK3β. GSK3 inhibits glycogen synthase activity
through phosphorylation. It plays a key role in various pathophysiological processes, such as differentiation,
immunity, metabolism, cell death, and cell survival. Therefore, GSK3 has evolved as an
important therapeutic target for treating neurological diseases, inflammatory diseases, and cancer. In
addition, GSK3 regulates inflammatory processes through NF-κB-induced expression of various
cytokines, including tumor necrosis factor-α (TNF-α), interleukin (IL)-1β, and IL-6. Moreover,
GSK3 is reported to participate in many signaling pathways related to disease pathology, including
PI3K/Akt, Wnt, Hedgehog, cyclic adenosine monophosphate, mitogen-activated protein kinase, and
transforming growth factor-β (TGF-β). GSK3 has become a therapeutic target against some inflammatory
diseases, including the inclusion body myositis, sepsis, and inflammatory bowel disease.
Hence, several GSK3 inhibitors have been under evaluation as new therapeutic strategies in recent
years. Two drugs targeting GSK3 have already entered clinical studies, including tideglusib and lithium
carbonate. In this study, we analyzed nearly 30 different small-molecule GSK3 inhibitors reported
in the past 4 years and classified them into four categories (thiazole, pyridine, F-substituted
benzene, and others) according to their structure to conduct further literature research. Moreover, we
summarized the optimal compounds and described the process of transformation from the lead compound
to the optimal compound. In addition, we aimed to summarize the role of GSK3 in the pathogenesis
of inflammatory diseases, with insights into the recent progress in the discovery of GSK3
inhibitors.
Herbal Therapies for Weight Gain and Metabolic Abnormalities Induced
by Atypical Antipsychotics: A Review Article
Authors: Naghibi Hamideh , Salari Roshanak , Yousefi Mahdi , Khadem-Rezaiyan Majid , Ghanbarzadeh Mohammad Reza , Fayyazi Bordbar Mohammad Reza
Published: April 2023

Psychosis is a state of mind that makes it difficult to determine what is real and what is not.
Psychosis can have serious negative effects. Like many psychiatric phenomena, psychosis has a variety
of causes, such as schizophrenia, bipolar disorder, and psychotic depression. Antipsychotic medications,
psychotherapy, and social support are the most common treatments. Antipsychotic drugs reduce
the symptoms of psychosis by changing brain chemistry. Based on the mechanism of action,
antipsychotics have two groups, typical and atypical. Most people who take antipsychotics experience
side effects. People taking typical antipsychotics tend to have higher rates of extrapyramidal side effects,
but some atypical drugs, especially olanzapine, are associated with the risk of significant weight
gain, diabetes, and metabolic syndrome, which, in turn, increases the risk of atherosclerotic cardiovascular
disease and premature death. Physical exercise, diet regimen, psychoeducation, monotherapy, or
switching to an alternative antipsychotic are strategies to correct metabolic aberrates in atypical antipsychotic
users. In light of several successful studies on the use of medicinal plants to control metabolic
syndrome, this article briefly reviews the studies on some herbal medications for the management
of metabolic disorders associated with atypical antipsychotics and discusses probable mechanisms.
Therefore, we searched the Cochrane, Scopus, PubMed, and Google Scholar databases for
works published before July, 2022, on the effect of herbal medications on antipsychotic-related metabolic
abnormalities in animals or humans. We recommend that some herbal medicines may be efficient
for regulating the metabolic changes related to atypical antipsychotics due to their multipotential
action, and more efforts should be made to make herbal drug treatments more effective. We hope this
review will be a reference for research on developing herbal therapeutics for metabolic alterations in
antipsychotic customers.
Lipidomics and Metabolomics in Infant Atopic Dermatitis: What’s the
Correlation with Early Nutrition?
Authors: Dessì Angelica , Di Maria Camilla , Pintus Roberta , Fanos Vassilios , Bosco Alice
Published: April 2023

To date, the complex picture of atopic dermatitis (AD) has not yet been fully clarified,
despite the important prevalence of this disease in the pediatric population (20%) and the possibility
of persistence into adulthood, with important implications for the quality of life of those affected, as
well as significant social and financial costs. The most recent scientific evidence suggests a new interpretation
of AD, highlighting the important role of the environment, particularly that of nutrition
in the early stages of development. In fact, the new indications seem to point out the harmful effect
of elimination diets, except in rare cases, the uselessness of chrono-insertions during complementary
feeding and some benefits, albeit weak, of breastfeeding in those at greater risk.

:

In this context, metabolomics and lipidomics can be necessary for a more in-depth knowledge of the
complex metabolic network underlying this pathology. In fact, an alteration of the metabolic contents
in children with AD has been highlighted, especially in correlation to the intestinal microbiota.
While preliminary lipidomic studies showed the usefulness of a more in-depth knowledge of the alterations
of the skin barrier to improve the development of baby skin care products. Therefore, investigating
the response of different allergic phenotypes could be useful for better patient management
and understanding, thus providing an early intervention on dysbiosis necessary to regulate the
immune response from the earliest stages of development.
Bioactive Compound Containing Hepatoprotective Activity
Authors: Yadav Amarjeet , Tiwari Nirbhay Narayan , Srivastava Shobhit Prakash , Tripathi Shivendra Mani , Mishra Sudhanshu
Published: April 2023

The liver is an essential organ that aids in the metabolism and elimination of xenobiotics
from the body and dysfunction of the liver is a serious health issue that concerns physicians, pharmaceutical
companies, and regulatory bodies. The effects of numerous hazardous substances, particularly
antibiotics, chemotherapeutics, carbon tetrachloride (CCl4), thioacetamide (TAA), and
microorganisms on liver cells have been extensively researched. Synthetic drugs used to treat liver
abnormalities in this condition can also harm the liver in other ways. The bioactivation of medicines
into chemically reactive metabolites, which can interact with cellular macromolecules like
proteins, lipids, and nucleic acids, causing protein malfunction, lipid peroxidation, DNA damage,
and oxidative stress, causes liver damage. Natural medications have proven to preserve normal and
useful liver stats while causing fewer adverse effects. The use of medicinal plants in the treatment
of liver disorders has a long history. This review article aims to gather information about prospective
phytochemicals from medicinal plants that have been investigated in modern scientific hepatotoxicity
models. During this review process, we concluded that some medicinal plants were showing
potent effects against liver damage due to their ability to minimize oxidative stress and modulate
the metabolic pathway responsible for liver damage.
Design, Synthesis, and Anti-tumor Activity of Novel 2-Aryl Benzimidazole Compounds
Authors: Xiaohelaiti Haimiti , Wu Wenping , Gao Yiting , Li Sisi , Ma Cheng
Published: April 2023

Background:

Combretastatin A-4 (CA-4) is a natural product isolated from the bark of the South African
bush willow tree Combretum caffrum, which exerts tubulin inhibition, but its clinical application is limited due to poor
stability and water solubility. 2-aryl benzimidazoles are excellent pharmacological skeletons with many activities,
especially in tumor inhibition, and better pharmacokinetic properties. Several scaffold CA-4 analogs have been synthesized
to date possessing antitumor activities.

Objective:

The benzimidazole was applied as the core moiety to replace the B ring and unstable linkage of CA-4, and
the 5-aryl acetenyl group was introduced to improve the antitumor activity. MCF-7, A549, Caco-2, Siha, and Eca-109
tumor cell lines were used to study inhibition by these agents in vitro.

Methods:

The benzimidazole structure was constructed from the oxidation of o-nitroaniline and aldehyde and the following
schemes, and the structural characterization was carried out. The antitumor effects were evaluated in vitro
through MTT assay, cell cycle arrest, and apoptosis assay. Molecular docking with tubulin (Protein ID: 1SA0) was
analyzed for the structure-activity relationship.

Results:

Among these derivatives, 4a-4h series (with 6-methoxy group) compounds inhibited the tumor cell lines
much stronger than the CA-4 and cisplatin, especially compound 4f showed prominently inhibitory activity in Siha cell
with IC50 value as 0.61 μmol/L. The further assay showed that the cell cycle was arrested at the G0/G1 phase as well
verified in apoptosis assay. Molecular docking indicated that 4f had stronger affinity energy and hydrogen bond than
CA-4.

Conclusion:

The compound 4f has the potency to be used as an anti-tubulin agent and the 2-trimethoxyphenyl benzimidazole
skeleton deserves further study as an antitumor structure.
Endoscopic Ultrasound-Guided Therapies in Patients with Pancreatic Neuroendocrine
Tumors
Authors: Morgan Amanda D. , Ramai Daryl , Bandaru Praneeth , Crino Stefano Francesco , Facciorusso Antonio
Published: April 2023

Pancreatic neuroendocrine neoplasms (PNETs) are rare but can be associated with significant morbidity and mortality. PNETs can be difficult to diagnose and have a propensity for metastasis before their diagnosis is established. To this end, many PNETs do not become apparent until late in their clinical course. Endoscopic ultrasound (EUS) has become the modality of choice for detecting these lesions due to its high tumor detection rate. Additionally, therapeutic tech-niques have arisen from EUS which allow for the treatment of PNETs. Overall, EUS has become a powerful diagnostic and therapeutic modality for addressing pancreatic lesions such as PNETs. In this perspective article, we provide an overview of the therapeutic potentials of EUS in the management of PNETs.
Explainable Artificial Intelligence (XAI) Approaches in Predictive
Maintenance: A Review
Authors: Sharma Jeetesh , Lal Mittal Murari , Soni Gunjan , Keprate Arvind
Published: April 2023

Predictive maintenance (PdM) is a technique that keeps track of the condition and performance
of equipment during normal operation to reduce the possibility of failures. Accurate
anomaly detection, fault diagnosis, and fault prognosis form the basis of a PdM procedure. This paper
aims to explore and discuss research addressing PdM using machine learning and complications
using explainable artificial intelligence (XAI) techniques. While machine learning and artificial intelligence
techniques have gained great interest in recent years, the absence of model interpretability
or explainability in several machine learning models due to the black-box nature requires further
research. Explainable artificial intelligence (XAI) investigates the explainability of machine learning
models. This article overviews the maintenance strategies, post-hoc explanations, model-specific
explanations, and model-agnostic explanations currently being used. Even though machine learningbased
PdM has gained considerable attention, less emphasis has been placed on explainable artificial
intelligence (XAI) approaches in predictive maintenance (PdM). Based on our findings, XAI
techniques can bring new insights and opportunities for addressing critical maintenance issues,
resulting in more informed decisions. The results analysis suggests a viable path for future studies.

Conclusion:

Even though machine learning-based PdM has gained considerable attention, less emphasis has been placed on explainable artificial intelligence (XAI) approaches in predictive maintenance (PdM). Based on our findings, XAI techniques can bring new insights and opportunities for addressing critical maintenance issues, resulting in more informed decisions. The results analysis suggests a viable path for future studies.
Construction and Clinical Application of Digital Intelligent Diagnosis and
Treatment System for Hepatocellular Carcinoma
Authors: Zeng Xiaojun , Tao Haisu , Lau Wan Yee , Fang Chihua
Published: April 2023

In the past 20 years, with the emergence and update of digital intelligent technology, the
diagnosis and treatment of hepatocellular carcinoma (HCC) have undergone profound changes.
Three-dimensional visualization technology has revolutionized the traditional two-dimensional diagnosis
and treatment model of HCC and realized preoperative visualization of tumors and complex
liver anatomy. The emergence of ICG fluorescence imaging has realized intraoperative tumor
boundary visualization from the molecular and cellular levels. Augmented reality (AR) and mixed
reality (MR) technology can realize the three-dimensional visualization of anatomical structures in
surgical navigation. Traditional experiential surgery has been transformed into modern intelligent
navigation surgery, and surgery has stepped into a new era of digital intelligent technology. In addition,
artificial intelligence, molecular imaging and nanoprobes are also expected to achieve early
diagnosis and treatment of HCC and improve the prognosis of patients. This article reviews the latest
application of digital intelligent diagnosis and treatment technology related to diagnosing and
treating HCC, hoping to help achieve accurate diagnosis and treatment of HCC.
Exploiting Predictability of Random Vector Functional Link Networks in
Forecasting Quality of Service (QoS) Parameters of IoT-Based Web
Services Data
Authors: Lenka Stitapragyan , Pradhan Sateesh Kumar , Nayak Samaleswari Prasad , Nayak Sarat Chandra
Published: April 2023

Background:

QoS parameters are volatile in nature and possess high nonlinearity, thus
making the IoT-based service and recommendation process challenging.

Methods:

An efficient and accurate forecasting model is lacking in this area and needs to be explored.
Though an artificial neural network is a prominent option for capturing such nonlinearities, its efficiency
is limited by the structural complexity and iterative learning method. The random vector functional
link network (RVFLN) significantly reduces the time complexity by randomly assigning input
weights and biases without further modification. Only output layer weights are calculated iteratively
by gradient methods or non-iteratively by least square methods. It is an efficient algorithm with low
time complexity and can handle complex domain problems without compromising accuracy. Motivated
by these characteristics, this article develops an RVFLN-based model for forecasting QoS parameter
sequences.

Results:

Two real-world IoT-enabled web service dataset series are used in developing and evaluating
the effectiveness of RVFLN-based forecasts in terms of three performance metrics.

Conclusion:

Experimental results, comparative studies, and statistical tests are conducted to establish
the superiority of the proposed approach over four other similar forecasting techniques.
Complementary Medication for COVID-19 Outbreak Concerns in the
Leprosy Community - A Passionate Proposal
Authors: Thangaraju Pugazhenthan , Venkatesan Sajitha , Gurunthalingam Meenalotchini Prakash , Velmurugan Hemasri , Sudha TY Sree , K Thangadurai , Thangaraju Eswaran , Balasundaram Mahesh Kumar
Published: April 2023

The number of COVID-19-positive cases continues to climb, causing alarm around the
world. The Nilavembu (Andrographis paniculata) Kudineer of Siddha medicine, an alternative
medicine practiced in India, has already been shown to be beneficial in viral outbreaks like
Chikungunya and Dengue fever. Several investigations have demonstrated that these herbs have
anti-inflammatory, analgesic, antipyretic, antibacterial, and antiviral activities against the Herpes
simplex virus and Ebstein Barr virus. This herbal remedy has also been shown to have a low risk of
negative side effects. There is currently no approved pharmaceutical treatment for COVID-19. Using
the phrases 'COVID-19,' 'Ayush,' 'Siddha,' and 'Leprosy,' we searched other databases such as
Google, Embase, ScienceDirect, and the Clinical Trial Registry of India. In another research on
Siddha treatment, Kabasura Kudineer, a decoction of 15 herbal items was discovered to have activity
against the spike protein of SARS COV-2 using an in-silico compound method. As there are
no effective treatments available for the prevention and treatment of mild to moderate cases, these
two medicines, Nilavembu Kudineer, and Kabasura Kudineer can be used as part of a COVID-19
prophylaxis using a combination of modern and alternative medicine.
Modified Adaptive Synchronization and Anti-Synchronization Method for Fractional Order Chaotic Systems with Uncertain Parameters
Authors: Agrawal S. K. , Batra Lalit , Mishra V. , Datta D.
Published: April 2023

In the present article, we have investigated the Adaptive synchronization
and Anti-synchronization between fractional order 3D autonomous chaotic system
and novel 3D autonomous chaotic system with quadratic exponential term using
Modified adaptive control method with unknown parameters. The modified adaptive
control method is very affective and more convenient in comparison to the existing
method for the synchronization of the fractional order chaotic systems. The chaotic
attractors and synchronization of the systems are found for fractional order time
derivatives described in Caputo sense. Numerical simulation results which are carried
out using Adams-Boshforth-Moulton method show that the method is reliable and
effective for synchronization and anti-synchronization of autonomous chaotic
systems.
Some Erdé lyi-Kober Fractional Integrals of the Extended Hypergeometric Functions
Authors: Jain S. , Goyal R. , Agarwal P. , Cesarano Clemente , Guirao Juan L.G.
Published: April 2023

This paper aims to establish some new formulas and results related to the
Erdélyi-Kober fractional integral operator applied to the extended hypergeometric
functions. The results are expressed as the Hadamard product of the extended and
confluent hypergeometric functions. Some special cases of our main results are also
derived.
IoT Based Website for Identification of Acute Lymphoblastic Leukemia using DL
Authors: Ambika R. , Thejaswini S. , Ramesh Babu N. , Hussain Sheikh Tariq , Bhat Nagaraj , Rasool Zafaryab
Published: April 2023

A form of cancer known as leukemia, attacks the body's blood cells and
bone marrow. This happens when cancer cells multiply rapidly in the bone marrow.
The uploaded image is analyzed by the website, and if leukemia is present, the user is
notified-a collection of pictures depicting leukemia as well as healthy bones and
blood. Once collected from Kaggle, the data is preprocessed using methods like image
scaling and enhancement. To create a Deep Learning (DL) model, we use the VGG-16
model. The processed data is used to “train” the model until optimal results are
achieved. A Hypertext Markup Language (HTML) based website is built to showcase
the model. Using a DL model, this website returns a response indicating whether or not
the user's uploaded photograph shows signs of leukemia. The primary aim of this site is
to lessen the likelihood that cancer cells may multiply while the patient waits for test
results or is otherwise unaware of their condition. Waiting for results after a leukemia
test can cause further stress and even other health problems, even if the person is found
to be leukemia-free. This problem can be fixed if this website is used as a screening
tool for leukemia. 
Innovative IoT-Based Wearable Sensors for the Prediction & Analysis of Diseases in the Healthcare Sector
Authors: Rao Vaddempudi Koteswara , Rahman H Ali Abdul , Al-Shenqiti Abdullah , Francis Britto Christopher , Krishnamoorthy N. , Abidi Aman
Published: April 2023

Health monitoring may be required regularly in everyday life, which might
help predict the significant health consequences. Accurate surveillance is required for
effective health parameters like temperature, stress, heart rate and blood pressure (BP)
in the medical and healthcare domains. The Ideal health-related characteristics for
efficient persistent health monitoring are established in this study. The primary goal of
the device is to monitor the health parameters of a person in everyday life, facilitating
psycho-physiological supervision to examine the relationship between underlying
emotional states, including changing stress levels, and the progression and prognosis of
cardiovascular disease. Non-invasive sensors are employed here to observe the
mentioned health-related variables. The observed data will be stored in the cloud for
further processing. IoT technology has been used to process and store the measured
parameters in the cloud. At the same time, the device will give a notification in the
form of an alarm to the concerned person. The data can be frequently monitored by the
guardian and the concerned doctor. This may help to keep an eye on the people even if
they are far away from the person and the stored data can be viewed at any time from
anywhere. Thus, the wearable device will record the health parameters of a person,
which may assist them to know their mental and physical health, as well as give alerts in case of abnormalities. Implementation of this system will be helpful for the
people to get an awareness about their health condition and also make them stay
healthy.
Glaucoma Detection Using Retinal Fundus Image by Employing Deep Learning Algorithm
Authors: Ilayarajaa K.T. , Sugadev M. , Devi Patil Shantala , Vani V. , Roopa H. , Kumar Sachin
Published: April 2023

Glaucoma is an eye disease that can result in permanent blindness if not
detected and treated in the early stages of the disease. The worst part of Glaucoma is
that it does not come up with a lot of visible symptoms, instead, it can go from the
preliminary stage to a serious issue quickly. A Deep Learning (DL) model is capable of
detecting the presence of Glaucoma by analyzing the image of the retina which is
uploaded by the user. In this research, two DL algorithms were used to detect the
presence of Glaucoma in the uploaded image. The DL algorithms include the
convolutional neural network or the CNN and the transfer learning algorithm. The
transfer learning algorithm is implemented by the VGG-19 model. Once two DL
models were developed using the above-mentioned algorithms, the models were trained
and tested using the images of the retina. The trained models are tested to find the
better model. The efficiency of the model is measured based on some metrics. These
metrics include the True Positive (TP), True Negative (TN), False Positive (FP), and
False Negative (FN). Using these metrics, the true positive rate, the true negative rate,
the false-positive rate, and the false-negative rate are calculated. From the above
values, the DL algorithm, which is more efficient than the other one in identifying
Glaucoma, can be found.
Implementation of the Deep Learning-based Website For Pneumonia Detection & Classification
Authors: Vedanarayanan V. , Cholli Nagaraj G. , Meleet Merin , Maurya Bharat , Appasami G. , Khurana Madhu
Published: April 2023

It is often difficult to diagnose several lung illnesses, such as atelectasis and
cardiomegaly, as well as Pneumonia, in hospitals due to a scarcity of radiologists who
are educated in diagnostic imaging. If pneumonia is diagnosed early enough, the
survival rate of pulmonary patients suffering from the disease can be improved. Most
of the time, chest X-ray (CXR) pictures are used to detect and diagnose pneumonia.
When it comes to detecting pneumonia on CXR images, even an experienced
radiologist may have difficulty. It is vital to have an automated diagnostic system to
improve the accuracy of diagnostic results. It is estimated that automated pneumonia
detection in energy-efficient medical systems has a substantial impact on the quality
and cost of healthcare, as well as on response time. To detect pneumonia, we employed
deep transfer learning techniques such as ResNet-18 and VGG-16. Each of the model's
four standard metrics, namely accuracy, precision, recall, and f1-score, are used to
evaluate. The best model is established by the use of metrics. To make pneumonia
detection simple, the website is designed by employing the best model.
Supplier performance evaluation factors for agroindustrial supply chain: A systematic review
Authors: Agustin Prima Dania Wike , Tafarel Aldo , Septifani Riska , Amer Yousef , Doan Linh
Published: April 2023

Supplier performance is critical in maintaining the stability of the supply chain activities in the agroindustry. Their performance needs to be assessed using relevant factors to choose reliable and dependable suppliers. A significant amount of research has been done to examine essential factors in selecting reliable suppliers in supply chains from various perspectives. The divergence of research findings has led to many assessment factors that must be considered when choosing the supplier. Therefore, this study aimed to analyze factors affecting agroindustry supplier performance using a systematic review. The research from reliable databases such as Elsevier, Emerald, Proquest, and Taylor & Francis from 2010 to 2020 has been analyzed. Similarity and relevance analysis has been applied to form the factors and eliminate the redundant and irrelevant factors. As a result, fourteen suppliers’ performance factors to support an effective supply chain system are identified. These factors include customer care, economics, quality improvement, stakeholder coordination, production performance, organizational improvement, human resources, food safety, risk factors, company image, market, geography, corporate social responsibility (CSR), and environmental management system (EMS).
Response surface methodology in the optimization of walur (Amorphophallus paeoniifolius var. Sylvestris) starch pregelatinization process
Authors: Bambang Widjanarko Simon , Ulandari Devy , Fibrianto Kiki
Published: April 2023

This study aimed to determine the effect of temperature and time of the pregelatinization process on the physical and functional properties of pregelatinized and native walur starch using the central composite design method of response surface methodology (CCD-RSM). Several analyses, including rapid visco analyzer (RVA), scanning electron microscopy (SEM), and X-ray diffraction (XRD), were used to characterize an optimum pregelatinized walur starch (PWS). The optimum conditions for producing PWS were at 87.51oC and 9.71 minutes. The experimental verification data, repeated three times, were not significantly different (P>0.05) from the prediction optimization data generated by the Design Expert Software 7.1.5 Trial Version, which produces PWS with 19.56 ± 0.68 % swelling, 9.87 ± 0.18 % solubility and 835.62 ± 0.84 % water holding capacity (WHC). The result from RVA analysis showed that the pregelatinization process of walur starch increases the peak, final and setback viscosity, peak time, and pasting temperatures but decreases breakdown viscosity. Native walur starch (NWS) had a more crystalline form than PWS on XRD analysis. The SEM analysis revealed that NWS had smooth surface granules compared to PWS granules.
Examining the role of forensic nursing for the needs of bereaved family care and the nursing record system
Authors: Yamada Noriko
Published: April 2023

Various scientists and medical practitioners believe care for bereaved families is lacking in Japan. Professor Noriko Yamada, Faculty of Nursing, Japanese Red Cross Akita College Of Nursing, believes a lack of focus on care for the bereaved is caused by the current emphasis on life-prolonging
treatment. She and her team are examining the role of forensic nursing in fulfilling the needs of bereaved families. In Japan, the range of responsibilities of nurses is narrow and regional, with expectations of nurses differing in different parts of Japan. This inconsistency can negatively
impact support for bereaved families and the police often take responsibility for providing care and support. This approach to care can lead to guilt among bereaved families and subsequent depression, chronic illness, soaring medical costs and even attempted suicide. Yamada and her team, which
includes clinical psychologist Kazuki Saito, is harnessing knowledge garnered from forensic medicine and preventive clinical forensic nursing to improve the level of care for bereaved families, benefiting affected individuals and Japanese society. Yamada has built a forensic recording software
tool for nursing intervention in the acute phase of disaster response to help early identification of victims. The app, called MiMoKA, can act as a tool to help the bereaved family in their mourning and grief.
Elucidation of the mechanisms underway in the brain during sleep with a focus on the circuit mechanisms that promote memory formation
Authors: Takeda Masaki
Published: April 2023

Scientists have been unable to fully elucidate the mechanisms at work in the human brain during sleep. A particularly mysterious area is memory formation during sleep. Professor Masaki Takeda, Research Center for Brain Communication, the Kochi University of Technology, Japan, leads
a team of researchers exploring brain behaviour and dynamism. A key focus is on human participants and what happens in their brains while sleeping, while a further element is looking at whether non-human primates have similar memory mechanisms to humans. A main goal for Takeda and the team
is uncovering the causal role of neural activities during sleep in memory consolidation. As such, the researchers are attempting to noninvasively manipulate neural activity when human participants are sleeping and investigate how these manipulations affect memory performance after sleep. Using
a combination of deep-learning decoding techniques and high spatiotemporal resolution of neural imaging by concurrent functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), the team has developed a novel technique for detecting the replay activity that emerges as unstructured
patterns of activations, overcoming challenges associated with the difference between spatial and semantic memory. In a recent project, Takeda and the team have been exploring brain circuit mechanisms during sleep that promote memory formation. The researchers are exploring whether the slow-oscillation
neural activity during deep sleep (SO) in the frontal cortex during deep sleep has a causal role in memory consolidation in humans. This involves the manipulation of the phase of SOs using transcranial alternating current stimulation (tACS) whose phase is matched to that of SOs.
Elucidating of the formation process of hierarchical networks in the visual cortex of mice
Authors: Murakami Tomonari
Published: April 2023

Among the complex connections in the brain are those in the cortical areas that control vision. There are research gaps concerning the visual neural network and its formation, and the early stages of corticocortical connection development. Assistant Professor Tomonari Murakami, The
University of Tokyo, is working to uncover unknowns in the field of neural circuit development. In his studies he is using mice models as, in mice, some of the development of neural circuits within the visual system occurs postnatally. Murakami is exploring, on a molecular level, the details
of the developmental processes involved in the formation of inter-regional connections. To do this, he is using comprehensive molecular expression analysis and genetic manipulation before and after the formation of the connections between the V1 and higher visual cortices. Subsequently, he
and his team will build upon this work, investigating whether the developmental strategy used in the visual system of mice is common with that of primates using marmoset monkeys. The researchers are using optogenetics to clarify the information propagation pathway of visual information and
plan to continue to clarify how the mammalian brain processes visual information and how the neural network composed of the higher visual cortex is formed during development. Ultimately, this will deepen understanding of the human brain.
The relationship between muscle deoxygenation and health-related physical fitness
Authors: Takagi Shun
Published: April 2023

Physical exercise is proven to be beneficial for health and studies have found that improved aerobic capacity is linked to reduced mortality. Dr Shun Takagi, Faculty of Education and Welfare, Biwako-Gakuin University, Japan, conducts research on the relationship between aerobic capacity
and muscle deoxygenation during aerobic exercise involving different forms of exercise and different populations. Most recently, he is investigating the relationship between muscle deoxygenation during resistance training and improvement of muscle strength and thickness by resistance training.
Enhancement of muscle deoxygenation during resistance exercise may be one of the determinants of muscle hypertrophy and increase in muscle strength and an improvement of muscle thickness and strength is related to improved quality of life. Therefore, the research could help enhance health
among adults. If Takagi observes through his studies a strong relationship between muscle deoxygenation and health-related physical fitness, muscle deoxygenation could prove to be a useful indicator for effective exercise training to improve health-related physical fitness. Takagi and the
team hope to be able to identify the determinants of muscle deoxygenation and then move onto developing effective training programmes for all generations, especially the more elderly. The researchers are utilising spatial resolved near infrared spectroscopy (SR-NIRS) in their work, as well
as a newly established optical fat correction method to overcome the fact that the variables measured by SR-NIRS are affected by light scattering in the fat layer. This method has enabled the team to overcome challenges and drive their research forward.
Development of gene therapy for hypophosphatasia
Authors: Miyake Koichi
Published: April 2023

Hypophosphatasia (HPP) is a disease caused by an inborn defect in the production of alkaline phosphatase that affects bones and teeth. The six types are perinatal lethal form, perinatal benign form, infantile form, childhood form, adulthood form and odontohypophosphatasia. Treatments
have historically been limited but, in 2015, an enzyme replacement therapy known as Asfotase Alfa was approved. This has improved life expectancy and quality of life but drawbacks such as requires 3-6 injections per week for life, injection pain and swelling at injection sites remain. Professor
Koichi Miyake and his team at the Department of Gene Therapy at Nippon Medical School (NMS), Japan, are working to improve treatments for HPP. The researchers are developing novel therapeutic agents to potentially treat HPP by only single injection, one of which is ARU-2801. The hope is that
this will not only prolong the life of HPP patients and improve their symptoms, but also improve their quality of life and will also improve the symptoms of patients with disease types that currently cannot be treated with enzyme replacement therapy. Miyake and the team have evaluated the
efficacy and safety of ARU-2801 using non-human primates (NHPs). In their studies, ARU-2801 was able to maintain tissue nonspecific alkaline phosphatase (TNALP) expression in NHPs and mice for a long period, with no side effects. In future clinical trials the effectiveness of ARU-2801 will
need to be confirmed in patients.
The T6SS toxins are powerful weapons for Pseudomonas' antibacterial strategy ‐ MRC
Authors: Filloux Alain , Nolan Laura
Published: April 2023

Antibiotic resistance is on the rise and an antibiotics crisis looms. Efforts to find new sources of antibiotics and antimicrobials have redoubled. This includes exploring the potential of repurposing drugs currently used against non-infectious diseases and seeking to discover new antimicrobials
in nature. Professor Alain Filloux, Imperial College London, is a world-leading expert in bacterial secretion systems who is investigating the type 6-secretion system (T6SS) of the multi-drug resistant bacterium P. aeruginosa. He is collaborating with Dr Laura Nolan in utilising a transposon
mutagenesis technique -transposon directed insertion-site sequencing (TraDIS) ‐ to search the P. aeruginosa genome for novel toxins secreted by the T6SS. Filloux and Nolan are conducting TraDIS both when the T6SS is active and when it is inactive, which will enable
them to identify the related toxin. So far, the researchers have validated the use of TraDIS by identifying T6SS immunity genes, and corresponding toxins, that were already characterised as part of the T6SS' arsenal, confirming that a global and unbiased technique has the
potential to identify novel toxins. By sequencing the mutant population, the team has also identified several potential novel toxin/immunity pairs, including Tse8/Tsi8, which targets the transamidosome involved in bacterial protein synthesis.