bluuebunny/crossref_metadata_2025 huggingface dataset:
Umbilical Stump Colonization and Antibiotic Susceptibility Patterns of Bacteria from Umbilical Stumps of Neonates Admitted at Holy Innocents Children’s Hospital, Mbarara, South Western Uganda
Authors: Mukundane Bruno , Bazira Joel , Musinguzi Benson , Byarugaba Frederick
Published: April 2023

Introduction: Every year a million deaths of neonates occur worldwide due to bacteria that has ascended through the umbilical stump leading to serious infection. An umbilical stump that has just separated is known to be the best route for bacterial entrance leading to development of serious infections. It also acts as a reservoir for bacteria which potentially may gain access to the blood flow within first 2–3 days of life when the umbilical vein is still patent. During neonatal period, the umbilical stump takes 1-2weeks to fall off under normal circumstances (WHO). Therefore understanding umbilical stump colonization is relevant within 14 days before the stump falls off. The most common isolated and recovered organisms that are known for colonization of the umbilical stump areStaphylococcus aureus, Escherichia coliand group BStreptococcus. No matter the type of colonizing bacteria, this kind of colonization normally leads to serious life-threatening infection.Objective: The objective of this study was to determine the umbilical stump colonization and antibiotic susceptibility patterns of bacteria isolated from umbilical stumps of neonates admitted at Holy Innocents Children’s Hospital.Methods: This study used a cross-sectional study and 200 participants were enrolled from neonatal ward in the period of May-June 2022. Samples were aseptically taken off from neonates using a sterile cotton swab which were then cultured and the colonizing organisms isolated after which antimicrobial susceptibility testing was done using Kirby Bauer disk diffusion method.Results: After culture of 200 samples, only 35 (17.5%) showed growth. Of the isolated organismsStaphylococcus aureushad the highest number of 15 (42.9%), this was followed byE.coliwith 8 (22.9%), thenK.pneumoniaeat 4 (11.4%),Proteus sppat 4 (11.4%, thenStreptococcus sppat 2 (5.7) and finallyPseudomonas sppat 2 (5.7%). AllStaphylococcus specieswere sensitive to Vancomycin whereas allE.colispecies were resistant to Imipenem, Tetracycline, Erythromycin and Ampicillin. AllKlebsiellaandStreptococcusspecies were 100% sensitive to Ciprofloxacin, Chloramphenicol and Gentamicin. Chloramphenicol had the highest sensitivity of 85.7% followed by Gentamicin at 62.9% and Trimethoprim-sulfamethoxazole had the highest resistance of 62.9% which was followed by Tetracycline with the resistance of 54.3%. AllStreptococcus specieswere sensitive to Cefoxitin, Chloramphenicol, Erythromycin and Clindamycin whereas allPseudomonasspecies were sensitive to Ceftazidime, Piperacillin and Piperacillin-tazobactam.Conclusion: The prevalence of umbilical stump colonisation at HICH was high at 17.5% and the main causative agents areStaphylococcus aureusandEscherichia.coli. These organisms are resistant to commonly used antibiotics like Ciprofloxacin, Ceftriaxone and Amoxicillin-Clavulanic acid.
In-situ study of microstructures induced by the olivine to wadsleyite transformation at conditions of the 410 km depth discontinuity
Authors: Ledoux Estelle , Krug Matthias , Gay Jeffrey , Chantel Julien , Hilairet Nadège , Bykov Maxim , Bykova Elena , Aprilis Georgios , Svitlyk Volodymyr , Garbarino Gaston , Guignot Nicolas , Sanchez-Valle Carmen , Speziale Sergio , Merkel Sébastien
Published: April 2023

The olivine-wadsleyite transformation is believed to occur at depths of about 410 km in the Earth, producing a major seismic discontinuity in this region of the Earth’s mantle. The mechanism of this phase transition controls the microstructures of the newly nucleated wadsleyite, the major phase of the upper part of the mantle transition zone, and thus impacts seismic observations in the region. Here, we study the microstructures produced by the olivine-wadsleyite transformation using in situ laboratory experiments at pressures and temperatures relevant for the mantle transition zone. We transform pure olivine samples in laser-heated diamond-anvil cells at pressures ranging from 12.3 to 20.2 GPa and temperatures of 1400–1730 K. At different steps of the transformation we measure the orientation and size distribution of individual sample grains using multigrain crystallography at synchrotron radiation sources. We find that the olivine to wadsleyite transformation is incoherent at the conditions of the mantle transition zone, and is probably dominated by nucleation of wadsleyite at grain boundaries of the parent olivine. Thus, we expect that seismic anisotropy near 410 km would drop significantly due to the randomized lattice preferred orientation of newly nucleated wadsleyite induced by the incoherent transformation.
A Simple Method for Determination of Chloroquine Based on Electrogenerated Chemiluminescence
Authors: Pacheco Jeovana , Brito Jhonathas , dos Santos Clenilton , Bezerra Cícero , Damos Flavio , Luz Rita
Published: April 2023

Chloroquine is a drug belonging to the aminoquinoline family that is widely used for treating diseases such as lupus, cancer, and malaria. In this sense, the present work describes the development of a simple method based on electrogenerated chemiluminescence (ECL) and a screen-printed carbon electrode (SPE) modified with carbon black for chloroquine determination. The carbon black modified SPE was characterized by scanning electron microscopy, Raman spectroscopy, cyclic voltammetry and linear sweep voltammetry to obtain the ECL-potential curves. The ECL method is based on the chemiluminescence resulting from the interaction between chloroquine and the tris(2’2-bipyridyl) ruthenium(II) complex. Under optimized experimental conditions, the method showed a wide linear working range between 0.5 and 500 µmol L-1. The method presented good precision and accuracy in drug samples used in the treatment of malaria and artificial urine sample, showing recovery values from 100 to 103% and 99 to 103%, respectively.
Thermoviscous liquid conjugate heat exchange simulation in cylindrical channel
Authors:   Mavlyutov Institute of Mechanics of UFRC RAS, Ufa, Russia, Moiseev K.V. , Iamlikhina I.N. ,   Ufa State Petroleum Technological University, Ufa, Russia
Published: April 2023

The article is concerned with the flow of a thermoviscous fluid in a cylindrical channel for various types of viscosity functions in the joint solution of the problem of conjugate heat exchange between the channel and the surrounding material. A model of a cylindrical channel with a thermoviscous liquid flowing inside at a given velocity and viscosity is considered. The methodology of the process consists in applying a system of model equations that describes the dynamics of thermoviscous fluid flow processes based on the laws of phase energy conservation, the continuity equation, the Navier-Stokes momentum conservation equation taking into account conjugate heat transfer and the heat transfer equation for a solid. Numerical implementation of model representations was carried out using the Comsol Multiphysics digital package. Velocity and temperature profiles were obtained in the cross section of the channel. The influence of the viscosity function on the flow profile is shown, as well as the quantitative characteristics of the flow and heat transfer. An analysis was made of the dependence of the nature of the velocity and temperature profiles on the viscosity of the liquid and the type of dependence of viscosity on temperature. The result of the work done is the found features and regularities that arise in the process of conjugate heat transfer of a thermoviscous liquid in a cylindrical channel when comparing the linear and exponential dependences of viscosity on temperature. The analysis of the obtained dependences of viscosity and temperature in the flow profile, the value of the maximum flow rate was carried out. It is shown that viscous friction causes a local increase in temperature near the pipe walls, followed by an increase in fluid velocity due to a decrease in viscosity. The results obtained are basic for the further development of this topic and will be used to solve new problems in the field of modeling the thermoviscous fluid flow and conjugate heat transfer.
The Future of PAH Treatment
Authors: Keen Jennifer L. , Al-Naamani Nadine , Ventetuolo Corey E.
Published: April 2023

Pulmonary arterial hypertension (PAH) is a devastating disease mediated by vasoconstriction and vascular remodeling of the pulmonary vasculature. Current therapies target the imbalance of vasoconstrictors and vasorelaxants in 3 pathways: nitric oxide, prostacyclin, and endothelin. While these have extended lifespans for PAH patients, significant morbidity and mortality remains. Notably, the progress in PAH therapy for over a decade has utilized these same 3 pathways. Fortunately, several new treatment options utilizing different mechanisms are emerging and will be reviewed here.
POINT: Is It Time to Lower the Cut-off for Increased Pulmonary Vascular Resistance? Yes
Authors: Triantafyllou Georgios A. , Maron Bradley A.
Published: April 2023

Background

For decades, pulmonary hypertension (PH) used to be defined by a mean pulmonary artery pressure (mPAP) ≥25 mm Hg; however, this criterion was not based on data that were systematically collected. With the availability of contemporary datasets however, it was evident that the upper limit of normal mPAP was ∼20 mm Hg, which is also the level of mPAP above which adverse outcomes increase. In addition, it is now evident that the specificity of mPAP >20 mm Hg to denote precapillary pulmonary vascular disease could be enhanced by adding pulmonary vascular resistance (PVR) to the precapillary PH definition. Finally, after characterizing large groups of normal individuals, akin to observations for mPAP, it was recently demonstrated that a PVR of ∼2.0 Wood units (WU) is the upper limit of normal, and the lower level associated with all-cause mortality in at-risk patients.

Clinical Implications

The current hemodynamic criteria for PH are positioned to capture more patients compared to the classical definition, with particular implications for earlier diagnosis. Importantly, pulmonary vasodilator therapies have not been tested adequately in patients with mPAP <25 mm Hg or PVR between 2 to 3 WU and, thus, should not be administered in these patients. Mild PH is an active focus of clinical trial design; at present, these patients should be referred to expert PH centers earlier for individualized therapeutic planning.

Conclusions

The revised definition of precapillary PH uses a PVR threshold of >2 WU. This value is evidence-based, and exceeding this threshold is associated with adverse clinical outcomes. This revision places focus on early diagnosis, close monitoring, and consideration for certain treatments. Further studies are needed that test the efficacy and safety of pulmonary arterial hypertension-specific therapy in precapillary PH patients with PVR 2 to 3 WU.
Anticancer Compounds from Cyanobacteria and their Implications in
Apoptosis
Authors: Gupta Amit , Singh Prashant R. , Singh Ashish P. , Kumari Neha , Jaiswal Jyoti , Sahu Niharika , Mishra Sonal , Pathak Jainendra , Sinha Rajeshwar P.
Published: April 2023

Cyanobacteria have been recognized as a rich source of bioactive metabolites with potential
biotechnological applications in the pharmacological industry. The chemically diverse natural compounds
or their analogues cause cytotoxicity. They may kill various cancer cells by inducing apoptosis
or changing the activation of cell signaling, particularly involving the protein kinase-C family of enzymes,
mitochondrial dysfunctions, and oxidative damage. B cell lymphoma 2 (Bcl-2) is an essential
component of apoptosis and is an antiapoptotic molecule. The key apoptotic regulators associated with
cancer are members of the Bcl-2 protein family, the key member of which is Bcl-2. The Bcl-2 protein
is a promising target for the emergence of new anti-tumor therapies because of its critical role in controlling
apoptosis. This review explores the significance of Bcl-2 in the onset of cancer; it may be used
as a target for developing high-quality drug therapies to treat various tumors. In addition, a number of
computational techniques were used to identify novel hit compounds that may act as inhibitors of the
apoptotic protein Bcl-2, including virtual screening, toxicity prediction, and drug-likeness analysis.
Twenty-three compounds were assessed as potential hits against Bcl-2, and these compounds were
subjected to ADMET property prediction. Dendroamide A and Welwitindolinone A appear to be the
most stable and effective drugs against Bcl-2 out of all those evaluated. This article gives an overview
of the bioactive compounds produced by cyanobacteria that have anticancer properties and may be
exploited to create novel anticancer medications in the future.
Neuroanatomical Correlates of the Late Positive Potential in Youth with
Pediatric Bipolar Disorder
Authors: Simonetti Alessio , Lijffijt Marijn , Kurian Sherin , Saxena Johanna , Janiri Delfina , Mazza Marianna , Carriero Giulio , Moccia Lorenzo , Mwangi Benson , Swann Alan C. , Soares Jair C.
Published: April 2023

Background:

The late positive potential (LPP) could be a marker of emotion dysregulation in youth with pediatric bipolar disorder (PBD). However, the neuroanatomical correlates of the LPP are still not clarified.

Objective:

To provide cortical and deep gray matter correlates of the LPP in youth, specifically youth with PBD.

Methods:

Twenty-four 7 to 17 years-old children with PBD and 28 healthy controls (HC) underwent cortical thickness and deep gray matter volumes measurements through magnetic resonance imaging and LPP measurement elicited by passively viewing of emotional faces through electroencephalography. T-tests compared group differences in LPP, cortical thickness, and deep gray matter volumes. Linear regressions tested the relationship between LPP amplitude and cortical thickness/deep gray matter volumes.

Results:

PBD had a more pronounced LPP amplitude for happy faces and a thinner cortex in prefrontal areas than HC.
While considering both groups, a higher LPP amplitude was associated with a thicker cortex across occipital and frontal lobes, and with a smaller right globus pallidus volume. In addition, a higher LPP amplitude for happy faces was associated with smaller left caudate and left globus pallidus volumes across both groups. Finally, the LPP amplitude correlated negatively with right precentral gyrus thickness across youth with PBD, but positively across HC.

Conclusion:

Neural correlates of LPP in youth included fronto-occipital areas that have been associated also with emotion processing and control. The opposite relationship between BPD and HC of LPP amplitude and right precentral gyrus thickness might explain the inefficacy of emotional control system in PBD.
Silybum marianum: An Overview of its Phytochemistry and Pharmacological Activities
with Emphasis on Potential Anticancer Properties
Authors: Nawaz Aisha , Zaib Sumera , Khan Imtiaz , Ahmed Abrar , Shahzadi Kiran , Riaz Huma
Published: April 2023

Silybum marianum (SM) belongs to the family Asteraceae, which holds therapeutic significance in medicinal chemistry. Phytochemistry analysis revealed an abundance of active constituents, particularly silymarin, composed
of polyphenols and flavonolignans. Silymarin is majorly found in leaves, seeds, and fruits and is comprised of seven
flavonolignans. Silymarin derivatives, specifically silybin, were reported for their medicinal properties. This review
summarizes the studies conducted to evaluate SM’s pharmacological properties and proposed mechanisms. SM exhibited anticancer properties due to being capable of modifying the induction of apoptosis, inhibiting the STAT3 pathway,
decreasing the transcription of various growth factors, impeding the growth of 4T1 cells and inducing cell cycle arrest
in various types of cancers, i.e., skin cancer, liver cancer, breast cancer, ovarian cancer etc. Silymarin and its derivatives protect the liver and ameliorate various immune-mediated and autoimmune hepatic diseases. Moreover, antimicrobial, antidiabetic, cardioprotective, nephroprotective, and neuroprotective activities were also reported. Based on
testified in vitro and in vivo studies, SM can serve as an alternative to cure various pathological ailments.
Non-linear Set-Valued Delay Functional Integral Equations of Volterra-Stieltjes Type: Existence of Solutions, Continuous Dependence and Applications
Authors: El-Sayed A. M. A. , M Al-Issa Sh. , Omar Y. M. Y.
Published: April 2023

In this chapter, we established two existence theorems for the non-linear
Volterra-Stieltjes integral inclusion. The continuous dependence of the solutions on
the delay functions, gi (i = 1,2) and on the set of selections, will be proved. The non-linear Chandrasekhar set-valued functional integral equation and a non-linear
Chandrasekhar quadratic functional integral equation, also the set-valued fractional
orders integral equation, are studied as an application. An initial value problem of
fractional-orders set-valued integro-differential equation will be considered.
Nanomaterials for Biosensing Applications
Authors: Tripathi Abhay Dev , Katiyar Soumya , Chaurasia Avinash K.
Published: April 2023

A biosensor is a device that detects the presence of analytes with its
biological receptor entity, having unique specificities corresponding to their analytes.
Most of these analytes are usually physical in nature, such as DNA, proteins,
antibodies, and antigens, but they may also be simple compounds, including glucose,
H2O2, toxins, and so on. Biosensors’ significance rises in providing real-time
quantitative and qualitative information on analyte composition. The sensing
mechanism involves the transduction of target binding interactions into optical,
electrochemical signals, etc ., which can be amplified and detected.
Nanomaterials (NMs) have shown significant potential in biological sensing-these
allow close interactions with target biomolecules due to their extremely small size and
suitable surface modifications. Nanomaterials appear to be potential possibilities
because of their capacity to immobilize a greater number of bioreceptor units in
confined devices and even act as a transduction element, allowing for enhanced
sensitivity and reduced detection limits down to specific molecules. Nanomaterials
have been widely used for in vitro detection of disease-related molecular biomarkers
and imaging, contrasts to map out the distribution of biomarkers in vivo. This chapter
summarizes nanomaterials such as gold nanoparticles, quantum dots, polymeric
nanoparticles, carbon nanotubes, nanodiamonds, and graphene nanostructured
materials that are currently being researched or utilized as biosensors.
AI and IoT-based Intelligent Management of Heart Rate Monitoring Systems
Authors: Venugopal Vedanarayanan , Mallapur Sujata V. , Kumar T.N.R. , Shanmugasundaram V. , Lakshminarayana M. , Kumar Ajit
Published: April 2023

The Heart Rate Monitoring system is developed using Artificial Intelligence
(ANN) and Internet of Things technology to detect the heartbeat and SpO2 of the
patient to monitor the risk of heart attack and also make the person do regular checkups. Health monitoring is very important to us to make sure our health is in which
condition. The proposed framework examines the sources of information gathered from
sensors fit with the patients to discover the crisis circumstance. The IoT technology is
employed; it helps anyone monitor the patient's health from anywhere. A deep learning
algorithm such as ANN is utilized to identify whether the person’s health is normal or
abnormal. In case an abnormality in health is noticed, an alert will be popped out. The
proposed framework with artificial intelligence and IoT can reduce the death occurring
due to heart rate, and other related issues can also be avoided.
Deep Learning Applications for IoT in Healthcare Using Effects of Mobile Computing
Authors: Rao Vaddempudi Koteswara , Shobha K.R. , Mateen Buttar Ahmed , Kumar Sonu , Aditya C.R. , Kumar Ajit
Published: April 2023

Diabetes is a chronic ailment characterized by abnormal blood glucose
levels. Diabetes is caused by insufficient insulin synthesis or by cells' insensitivity to
insulin activity. Glucose is essential to health since it is the primary source of energy
for the cells that make up a person's muscles and tissues. On the condition that if a
person has diabetes, his or her body either does not create enough insulin or cannot
utilize the insulin that is produced. When there isn't enough insulin or cells stop
responding to insulin, many dextroses accumulate in the person's vascular framework.
As time passes, this could lead to diseases such as kidney disease, vision loss, and
coronary disease. Although there is no cure for diabetes, losing weight, eating
nutritious foods, being active, and closely monitoring the diabetes level can all assist.
In this research, we used Artificial Neural Network to create a Deep Learning (DL)
model for predicting Diabetes. Then it was validated using an accuracy of 92%. In
addition, with the help of the MIT website, a mobile application was constructed. This
project will now assist in predicting the effects of diabetes and deliver personalized
warnings. Early detection of pre-diabetes can be extremely beneficial to patients since
studies have shown that symptoms of early diabetic difficulties frequently exist at the
time of diagnosis.
Design and Development of Deep Learning Model For Predicting Skin Cancer and Deployed Using a Mobile App
Authors: Madiwal Shweta M , Sudhakar M. , Subramanian Muthukumar , Venkata Srinivasulu B. , Nagaprasad S. , Khurana Madhu
Published: April 2023

Melanoma, the deadliest form of skin cancer, is becoming more common
every year, according to the American Cancer Society. As a result of the artifacts, low
contrast, and similarity to other lesions, such as moles and scars on the skin, diagnosing
skin cancer from the lesions might be difficult. Skin cancer can be diagnosed using a
variety of techniques, including dermatology, dermoscopic examination, biopsy and
histological testing. Even though the vast majority of skin cancers are non-cancerous
and do not constitute a threat to survival, certain more malignant tumors can be fatal if
not detected and treated on time. In reality, it is not feasible for every patient to have a
dermatologist do a complete examination of his or her skin at every visit to the doctor's
office or clinic. To solve this challenge, numerous investigations are being conducted
to provide computer-aided diagnoses. In this work, skin cancer can be predicted from
an image of the skin using deep learning techniques such as convolutional neural
networks. The accuracy and loss functions of the model are used to evaluate its overall
performance. The mobile app is created to detect skin cancer using the developed
model. As soon as the images have been submitted, the app can communicate with the
user about their progress.
Feature Extraction and Diagnosis of Dementia using Magnetic Resonance Imaging
Authors: Gupta Praveen , Kumar Nagendra , Ajad  , Arulkumar N. , Subramanian Muthukumar
Published: April 2023

Dementia is a state of mind in which the sufferer tends to forget important
data like memories, language, etc.. This is caused due to the brain cells that are
damaged. The damaged brain cells and the intensity of the damage can be detected by
using Magnetic Resonance Imaging. In this process, two extraction techniques, Gray
Level Co-Occurrence Matrix (GLCM) and the Gray Level Run-Length matrix
(GLRM), are used for the clear extraction of data from the image of the brain. Then the
data obtained from the extraction techniques are further analyzed using four machine
learning classifiers named Support Vector Machine (SVM), K-Nearest Neighbor
(KNN), Random Forest (RF), and the combination of two classifiers (SVM+KNN).
The results are further analyzed using a confusion matrix to find accuracy, precision,
TPR/FPR - True and False Positive Rate, and TNR/FNR – True and False Negative
Rate. The maximum accuracy of 93.53% is obtained using the GLRM Feature
Extraction (FE) technique with the combination of the SVM and KNN algorithm.
Deep Learning-Based Regulation of Healthcare Efficiency and Medical Services
Authors: Mohana T. Vamshi , Buradkar Mrunalini U. , Alaskar Kamal , Sheikh Tariq Hussain , Kumbhkar Makhan
Published: April 2023

There has been an increase in new diseases in recent years, which has had
both economic and societal consequences. Patients in the modern environment require
not only constant monitoring but also all-encompassing smart healthcare solutions.
These systems keep track of the patient's health, store data, and send alerts when
critical conditions arise. Healthcare may be considerably improved with the use of
Artificial Intelligence and Machine Learning (ML) systems. These systems can help
with earlier diagnosis of diseases, as well as more specific treatment plans. As big data,
the Internet of Things with many more smart technologies grows more widespread;
deep learning is becoming more popular. Due to the apparent rising complexity and
volume of data in healthcare, artificial intelligence (AI) will be used more frequently.
This work aims to develop a deep learning-based smart healthcare monitoring system.
This system keeps track of patients' health, analyses numerous parameters, categorizes
data, and organizes requirements. The algorithm using the python program is
developed and discussed to track the health of several patients with various illnesses.
This method also aids in the categorization of data, organization of pharmacological
requirements. This approach yields satisfactory performance, and the results are also
provided.
An Efficient Design and Comparison of Machine Learning Model for Diagnosis of Cardiovascular Disease
Authors: Narayan Sahu Dillip , Sudhakar G. , G Raju Chandrakala , Joshi Hemlata , Kumbhkar Makhan
Published: April 2023

Cardiovascular disease has a significant global impact. Cardiovascular
disease is the primary cause of disability and mortality in most developed countries.
Cardiovascular disease is a condition that disturbs the structures and functionality of
the heart and can also be called heart disease. Cardiovascular diseases require more
precise, accurate, and reliable detection and forecasting because even a small
inaccuracy might lead to fatigue or mortality. There are very few death occurrences
related to cardio sickness, and the amount is expanding rapidly. Predicting this disease
at its early stage can be done by employing Machine Learning (ML) algorithms, which
may help reduce the number of deaths. Data pre-processing can be employed here to
eliminate randomness in data, replace missing data, fill in default values if appropriate,
and categorize features for forecasting and making decisions at various levels. This
research investigates various parameters that are related to the cause of heart disease.
Several models discussed here will come under the supervised learning type of
algorithms like Support Vector Machine (SVM), K-nearest neighbor (KNN), and Naïve
Bayes (NB) algorithm. The existing dataset of heart disease patients from the Kaggle
has been used for the analysis. The dataset includes 300 instances and 13 parameters
and 1 label are used for prediction and testing the performance of various algorithms.
Predicting the likelihood that a given patient will be affected by the cardiac disease is the goal of this research. The most important purpose of the study is to make better
efficiency and precision for the detection of cardiovascular disease in which the target
output ultimately matters whether or not a person has heart disease.
Deep Learning Based Object Detection Using Mask R-CNN
Authors: Gupta Vinit , Mandloi Aditya , Pawar Santosh , Aravinda T.V , Krishnareddy K.R
Published: April 2023

Nowadays, an image must be verified or analyzed keenly for further
processing methods. Few numbers of images can be analyzed manually for a particular
object or a human being. But when millions and millions of images are present in a
dataset, and every single one of them must be verified and classified based on the
objects present in the image, it is necessary to find an algorithm or a technique to assist
this process. Of many types and applications of object detection, this research aims at
pedestrian detection. Pedestrian detection is a special way that only aims to detect the
human beings in the uploaded image. The Mask R-CNN algorithm is used in pedestrian
detection. The Mask R-CNN model is a Deep Learning (DL) model that can detect a
certain object from an image and can also be used for image augmentation. The Mask
R-CNN stands for Mask Regional Convolutional Neural Network (CNN). This is one
of the classification techniques which can be designed using the Convolutional
Network theories. The Mask R-CNN algorithm is the updated version of the Faster RCNN. The main difference between the faster R-CNN and the Mask R-CNN is that the
mask R-CNN can bind the borders of the object detected while the faster R-CNN uses a
box to identify the object. One of the major advantages of mask R-CNN is that it can
provide high-quality image augmentation and is also one of the fastest image
segmentation algorithms. This model can be easily implemented when compared to
other object detection techniques. Python contains various inbuilt dependencies which
can be installed with the help of repositories. These dependencies are used to design the
model. The model is then trained and tested with various inputs for better accuracy.
Image segmentation is used in various fields like medical imaging, video surveillance,
traffic control, etc., so the mask R-CNN technique would be an extremely efficient DL
algorithm.
Design and Comparison Of Deep Learning Architecture For Image-based Detection of Plant Diseases
Authors: Upadhyaya Makarand , Nagendrappa Malvade Naveen , Kumar Shukla Arvind , Walia Ranjan , Devi K Nirmala
Published: April 2023

 Agriculture provides a living for half of India's people. The infection in
crops poses a danger to food security, but quick detection is hard due to a lack of
facilities. Nowadays, Deep learning will automatically diagnose plant diseases from
raw image data. It assists the farmer in determining plant health, increasing
productivity, deciding whether pesticides are necessary, and so on. The potato leaf is
used in this study for analysis. Among the most devastating crop diseases is potato leaf
blight, which reduces the quantity and quality of potato yields, significantly influencing
both farmers and the agricultural industry as a whole. Potato leaves taken in the
research contain three categories, such as healthy, early blight, and late blight.
Convolution Neural Network (CNN), and Convolution Neural Network- Long Short
Term Memory(CNN-LSTM) are two neural network models employed to classify plant
diseases. Various performance evaluation approaches are utilized to determine the best
model.
Deploying Deep Learning Model on the Google Cloud Platform For Disease Prediction
Authors: Aditya C.R. , Sekhar Kolli Chandra , Swaroopa Korla , Hemavathi S. , Karajgi Santosh
Published: April 2023

A brain tumor is defined by the proliferation of aberrant brain cells, some of
which may progress to malignancy. A brain tumor is usually diagnosed via a magnetic
resonance imaging (MRI) examination. These images demonstrate the recently
observed aberrant brain tissue proliferation. Several academics have examined the use
of machine learning and Deep Learning (DL) algorithms to diagnose brain tumors
accurately A radiologist may also profit from these forecasts, which allow them to
make more timely decisions. The VGG-16 pre-trained model is employed to detect the
brain tumor in this study. Using the outcomes of training and validation, the model is
completed by employing two critical metrics: accuracy and loss. Normal people
confront numerous challenges in scheduling a doctor's appointment (financial support,
work pressure, lack of time). There are various possibilities for bringing doctors to
patients' homes, including teleconferencing and other technologies. This research
creates a website that allows people to upload a medical image and have the website
predict the ailment. The Google Cloud Platform (GCP) will be utilized to install the DL
model due to its flexibility and compatibility. The customized brain tumor detection
website is then constructed utilizing HTML code. 
Classification and Diagnosis of Alzheimer’s Disease using Magnetic Resonance Imaging
Authors: Shobha K.R. , Gajendra Shende Vaishali , Patil Anuradha , Kumar Ega Jagadeesh , Kumar Kaushalendra
Published: April 2023

Different types of brain illnesses can affect many parts of the brain at the
same time. Alzheimer's disease is a chronic illness characterized by brain cell
deterioration, which results in memory loss. Amnesia and ambiguity are two of the
most prevalent Alzheimer's disease symptoms, and both are caused by issues with
cognitive reasoning. This paper proposes several feature extractions as well as Machine
Learning (ML) algorithms for disease detection. The goal of this study is to detect
Alzheimer's disease using magnetic resonance imaging (MRI) of the brain. The
Alzheimer's disease dataset was obtained from the Kaggle website. Following that, the
unprocessed MRI picture is subjected to several pre-processing procedures. Feature
extraction is one of the most crucial stages in extracting important attributes from
processed images. In this study, wavelet and texture-based methods are used to extract
characteristics. Gray Level Co-occurrence Matrix (GLCM) is utilized for the texture
approach, and HAAR is used for the wavelet method. The extracted data from both
procedures are then fed into ML algorithms. The Support Vector Machine (SVM) and
Linear Discriminant Analysis (LDA) are used in this investigation. The values of the
confusion matrix are utilized to identify the best technique. 
Metastatic calcinosis cutis in a dialysis patient
Authors: Bapoo Nabeel , Chothia Mogamat-Yazied , Stead Piers A , Ally Ismail
Published: April 2023

Metastatic calcinosis cutis is an uncommon complication of end-stage kidney disease but has severe and disabling effects. Its development is attributed to disorders of calcium and phosphate metabolism associated with secondary hyperparathyroidism. The mainstay of treatment remains medical therapy; however, in refractory cases a parathyroidectomy is indicated. We describe the case of a 22-year-old female with refractory hyperparathyroidism treated with a subtotal parathyroidectomy resulting in complete resolution of metastatic calcinosis cutis of the hands. Clinicians should be aware that this complication can occur soon after the initiation of dialysis and that rapid complete resolution can be achieved with parathyroidectomy.
Development of QUARIN-J, a Network for Providing Educational Resources for Qualitative Research Papers
Authors: Kayama Mami
Published: April 2023

Qualitative research harnesses thoughts, concepts or experiences and provides in-depth understanding. Nursing is a field that relies heavily on qualitative research but the diversity of qualitative research methods means there are sometimes issues during the peer review process of nursing
research papers. Mami Kayama is Research Director and an expert co-investigator on this team of researchers at the National College of Nursing, Tokyo, Japan, working on the dissemination of qualitative research methods in nursing. In addition to Kayama, the team comprises co-investigators
Yumi Nishimura, Kiyomi Asahara, Yuriko Miura, Yasuhito Kinoshita, Kyoko Oyamada, Noriko Yamamoto-Mitani and Misuzu Gregg. Ultimately, they want to promote the development of the field. Kayama believes that the biggest difficulty facing the dissemination of qualitative nursing studies is matching
papers and reviewers. The researchers have developed a network for providing educational resources for qualitative research papers called QUARIN-J (Qualitative Research Implementation Network of Nursing-Japan). The goal of the QUARIN-J website is to promote the publication
of qualitative research that contributes to the development of nursing science and it provides resources such as guidelines and peer review experiences that can assist with this. There are two guidelines for qualitative research methods on the website: a checklist developed by the research
group that demonstrates what a good qualitative research paper should look like; and the Japanese version of Standards for Reporting Qualitative Research (SRQR). There are also personal essays describing how members of the research group have personally experienced the peer review process.
Exploring educational program for specialisation of the primary care nursing role
Authors: Kako Mayumi , Moriyama Michiko , Mizukawa Mariko
Published: April 2023

In countries including Australia, Canada, Ireland, the UK and Spain, primary care nursing (PCN) is situated as a part of the nursing education but it is not well recognised in Japan. As Japan's society continues to age, there is a shift underway from hospital to
community care and the role of nurses in the community is arguably more important than ever before. There are misconceptions around the role of primary care nurses, including the incorrect assumption that their role is that of a doctor's assistant. Rather, their roles are
far more complex and require a people-centred approach. Dr Mayumi Kako, School of Biomedical and Health Sciences, Division of Nursing Science (International Disaster Nursing), Hiroshima University, Japan, is working to improve understanding of and appreciation for the role of primary care
nurses. This research involves developing educational programmes to improve awareness of the professional role of PCN. She and her team are drawing comparisons between the concept of PCN in the basic nursing educational curriculum in Japan and PCN education in Australia, Canada, Spain, UK,
and Ireland in order to identify contrasts and enhance awareness. If they can better understand how the PCN concept is positioned in other countries, they will be able to identify gaps between PCN curriculums in these countries and Japan, and investigate how the PCN concept could be embedded
into the Japanese nursing curriculum in the future.
Creation of a conceptual model for understanding subtle reactions of children with severe physical and mental disabilities
Authors: Yokozeki Emiko , Yamamoto Kohji
Published: April 2023

Advances in medical technology have assisted with the provision of care for children with severe physical and mental disabilities, helping to improve and maintain their living standards, and enabling a high level of care to be given at home. But there remains a burden on the families
delivering care, particularly when communication is difficult. Researchers in the Society 5.0 Study Group, Interdisciplinary Research Institute, Shikoku University, Japan, are exploring the potential of information and communication technology (ICT) to be used to improve the quality of care
for children with severe physical and mental disabilities. The researchers are developing a conceptual model for understanding the subtle reactions of children, including the sensing of biological reactions such as facial muscle movement. The goal is to better understand the reactions of children
with severe disabilities, leading to improved levels of care and benefiting the families of patients. Dr Emiko Yokozeki leads the team. She has three decades of nursing experience and is a member of the Society 5.0 Research Society, which is chaired by Professor Kohji Yamamoto and promotes
research with the aim of creating a society where everyone can live safely and comfortably by utilising innovation.
Ongoing collaborative project for building a symbiotic society with dementia: the analysis of Alzheimer's pathogenic changes by 'ageing' of disease-relevant proteins.
Authors: Ito Genta , Utsunomiya-Tate Naoko
Published: April 2023

In Japan, the ageing society correlates to a significant burden of care for elderly people with dementia. Researchers are exploring the epidemiology of the disease, as well as the social aspects. This includes understanding what a well-functioning society encompassing people with dementia
looks like. Professor Naoko Utsunomiya-Tate, Laboratory Head at the Laboratory of Biomolecular Chemistry, Faculty of Pharma-Sciences, Teikyo University, Japan, is engaged in two key research strands: 'Building a symbiotic society with dementia' and 'Biophysical
and structural chemical research on the racemisation of the pathogenic protein in the onset process of Alzheimer's disease'. Utsunomiya-Tate envisions a symbiotic society in which people with dementia aren't tucked away in care homes
and healthcare facilities but, rather, well integrated into, and respected by, society. She and her team are working to develop diagnostic, preventative and treatment measures and anticipate possible issues in advance of implementing these in society in order to develop solutions. Utsunomiya-Tate
is collaborating with Senior Assistant Professor Genta Ito and an interdisciplinary team of researchers to identify pathogenic protein variants that suppress fibrillisation and aggregate formation, to elucidate the mechanism underlying the cell-to-cell transfer of Tau aggregates, and to develop
gene therapy targeting the etiological proteins of dementia. The team also aims to understand how D-isomerisation of the amino acids constituting amyloid β and Tau protein is involved in the process of their fibrillisation and aggregation.
Development of Biodegradable Small Diameter Vascular Graft
Authors: Oyama Kyohei
Published: April 2023

Coronary heart disease (CHD) is a major cause of death on a global scale. Treatment options include revascularisation, but achieving a successful vascular graft requires using one with a diameter of less than 4mm and there is no artificial vascular graft at such a small diameter available.
Scientists are interested in developing biodegradable artificial vascular grafts that can help overcome challenges associated with CHD. Associate Professor Kyohei Oyama and his team in the Department of Cardiac Surgery, Asahikawa Medical University, Japan, are currently working on two main
projects: Creating a biodegradable small diameter vascular graft; and developing a heart regeneration treatment via endogenous cardiac myocyte proliferation. In developing a biodegradable small diameter vascular graft, the researchers hope to benefit patients that require the revascularisation
of small vessels and also positively impact young patients who are still growing. The team also hope that the discoveries made through this research might lead to advances in other organ regeneration. When it comes to the need to recover lost cardiac tissue, this is unable to regenerate as
cardiac myocytes (muscle cells) have no ability to proliferate. Oyama and the team are investigating the mechanism that limits this proliferation ability with a view to activating it. If cardiac myocyte proliferation can be activated during the revascularisation of CHD, then the damaged heart
can be completely cured and the possibility of heart failure eliminated.
Diagnosis of strangulated bowel obstruction using blood DNA-degrading enzyme activity
Authors: Iwai Takuma
Published: April 2023

Strangulated bowel obstruction (StBO) is an abdominal emergency that can be fatal if left untreated. However, it can be difficult to diagnose due to a lack of consensus regarding the diagnosis of StBO based on blood biochemistry or arterial blood gases, inconsistencies with imaging
used in diagnosis and a lack of definitive features that definitively point to StBO. Assistant Professor Takuma Iwai, Nippon Medical School, Japan, is leading a team of researchers developing a novel method to easily diagnose StBO using peripheral blood. This will help with diagnosis, improve
outcomes and save lives. In this work, Iwai is applying his longstanding experience in laparoscopic surgery and chemotherapy for gastrointestinal cancers. The researchers are investigating how Cell-free DNA (cfDNA) can be used in StBO diagnosis. This began with research on the treatment of
colorectal cancer utilising cfDNA, which led to the discovery that the length of cfDNA fragments present in blood is indicative of various forms of cellular death - and that variations in DNA fragment length also result in differences in enzymatic degradation activity.
This led Iwai and the team to posit that cfDNA may also prove beneficial in the diagnosis of StBO, which is characterised by a disturbance in blood flow.
Elevating global health together
Authors: Annette Lucy
Published: April 2023

The World Health Summit (WHS) is an international meeting for experts to discuss the most important issues facing global health care. The 2022 event was co-hosted by WHO, with more than 300 speakers and over 40 WHO experts. In his opening ceremony speech, WHO Director- General Tedros
Adhanom Ghebreyesus drew links between the first summit in 2009, which was the year of the H1N1 pandemic and today's backdrop of the ongoing COVID-19 pandemic. He said that the theme of the 2022 WHS is 'taking global health to a new level'
through a shared response and commitment, a new global architecture and a new global approach. He explained that this requires a new agreement based on 'a common vision, a new global health architecture that is coherent and inclusive, and a new global approach that prioritizes
promoting health and preventing disease, rather than only treating the sick'. As such, a new agreement or global accord will be established, ensuring nations of the world commit to bringing this vision to life. Ghebreyesus said that this global accord will 'provide
an essential framework and foundation for other initiatives to keep the world safe' and will be based on a shared commitment to solidarity and equity, with countries working together rather than in competition with each other. Indeed, the countries will be involved in negotiating
the agreement and it will be implemented according to their respective national laws.
Building a versatile medical test system in a pandemic
Authors: Oba Junna , Toriya Masako , Matsuo Koichi
Published: April 2023

The COVID-19 pandemic was unprecedented and forced countries to organise and combine resources and develop coordinated responses. Temporary systems were established with the support of researchers and other stakeholders. Dr Junna Oba, Assistant Professor at the Department of Extended
Intelligence for Medicine at Keio University School of Medicine (KUSM) is devising methodologies for developing adjustable temporary systems that are quick and efficient to build, apply and dissemble when emergency situations have passed. KUSM helped to respond to the COVID-19 crisis by collaborating
with basic science researches to reinforce the PCR testing system in hospitals. Among those involved in this effort were Professor Masako Toriya of Keio University Global Research Institute (KGRI) and Professor Koichi Matsuo from the KUSM's Collaborative Research Resources
group. The researchers built from scratch a workflow for effective testing and information sharing. Since this time, the team has been working to build a cooperative network, developing a novel approach to planning, analysing and recording various processes and strategies to address emergency
situations that will be shared with the wider healthcare community and policy makers. The researchers used systems engineering methods to launch an unprecedented collaborative system that offers the ability to gain an overall picture of the interrelationships between departments that are working
together on the diagnosis, treatment and management of COVID-19.