bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting the Secondary Structure of Proteins: A Deep Learning Approach
Authors: Mehrotra Deepti , Kathuria Charu , Misra Navnit Kumar 
Published: October 2022

Background:
The machine learning computation paradigm touched new horizons with the development of deep learning architectures. It is widely used in complex problems and achieved significant results in many traditional applications like protein structure prediction, speech recognition, traffic management, health diagnostic systems and many more. Especially, Convolution neural network (CNN) has revolutionized visual data processing tasks.

Objective:
Protein structure is an important research area in various domains extending from medical science, health sectors to drug designing. Fourier Transform Infrared Spectroscopy (FTIR) is the leading tool for protein structure determination. This review aims to study the existing deep learning approaches proposed in the literature to predict proteins' secondary structure and to develop a conceptual relation between FTIR spectra images and deep learning models to predict the structure of proteins.

Methods:
Various pre-trained CNN models are identified and interpreted to correlate the FTIR images of proteins containing Amide-I and Amide-II absorbance values and their secondary structure.

Results:
The concept of transfer learning is efficiently incorporated using the models like Visual Geometry Group (VGG), Inception, Resnet, and Efficientnet. The dataset of protein spectra images is applied as input, and these models act significantly to predict the secondary structure of proteins.

Conclusion:
As deep learning is recently being explored in this field of research, it worked remarkably in this application and needs continuous improvement with the development of new models.
Would the Development of a Multitarget Inhibitor of 3CLpro and
TMPRSS2 be Promising in the Fight Against SARS-CoV-2?
Authors: dos Santos Nascimento Igor José , de Moura Ricardo Olimpio 
Published: October 2022

Abstract:
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2), responsible for generating
COVID-19, has spread worldwide and was declared a pandemic by the World Health Organization
(WHO) on 11 March 2020, being responsible for various damages to public health, social life,
and the economy of countries. Its high infectivity and mutation rates have stimulated researchers and
pharmaceutical companies to search for new therapies against this disease. These efforts resulted in
several vaccines and the identification of Molnupiravir as an oral treatment for this disease. However,
identifying new alternatives and critical information is necessary to fight against this devastating
agent. The findings in recent years regarding the structure and biochemistry of SARS-CoV2 are remarkable.
In anti-CoV drug discovery, various targets, such as structural, non-structural, and hostrelated
proteins are explored. In fact, 3CLpro is the most used among non-structural proteins since this
protease cleaves peptide sequences after the glutamine residue, and no human protease has this function.
This makes this macromolecule an excellent drug target for discovering new compounds. Another
promising target is the transmembrane protease serine 2 (TMPRSS2). Recent studies point to
TMPRSS2 as one of the main targets responsible for viral entry related to the cleavage of the S protein.
Similar to cathepsins, TMPRSS2 is also responsible for cleaving the spike protein SARS-CoV2,
which binds to the ACE2 receptor. Thus, TMPRSS2 is one of the targets that may represent new
alternatives in treating SARS-CoV2. In this context, would discovering a multitarget inhibitor be the
new strategy in searching for drugs against SARS-CoV2? For many years, new drug discovery was
based on the "one drug, one target" premise, where the biological action is related to interactions
with only one biological target. However, this paradigm has been overcome as new evidence of multiple
mechanisms of action for a single drug. Finally, this review will present a perspective on drug
design based on a multitarget strategy against 3CLpro and TMPRSS2. We hope to provide new horizons
for researchers worldwide searching for more effective drugs against this devastating agent.
A Comprehensive and Critical Review on Analytical and Bioanalytical methods for Metformin Hydrochloride, Dapagliflozin, and Saxagliptin
Authors: Desai Saloni , Maradia Rajnikant Bhikhubhai , Suhagia Bhanubhai N 
Published: October 2022

Abstract:New antidiabetic drug combination Metformin Hydrochloride, Dapagliflozin, and Saxagliptin have been recently approved for type II diabetes. This is marketed by Takeda Pharmaceuticals under the brand name Qternmet XR. Although different analytical and bioanalytical methods using different techniques such as liquid chromatography, high-performance liquid chromatography, high-performance thin-layer chromatography, gas chromatography, spectrophotometry, spectrofluorimetric methods coupled with ultraviolet, fluorescence, mass, or tandem-mass spectrometry detection have already been developed for the determination of Metformin Hydrochloride, Dapagliflozin, and Saxagliptin. Sensitive, cost-effective, and more optimized methods are yet required. Therefore, this review summarizes the main analytical and bioanalytical aspects regarding not only simultaneous estimation but also stability-indicating methods, kinetic studies, and impurity analysis for the analysis of proposed drugs in bulk and pharmaceutical dosage forms. Thus, this review gathers, for the first time, important background information on all analytical and bioanalytical methods that have been developed and applied for the determination of Metformin Hydrochloride, Dapagliflozin, and Saxagliptin, which should be considered as a starting point if new techniques are aimed to be implemented for these drugs.
Nanotechnology a Boon for Colorectal Cancer Treatment
Authors: Kriplani Priyanka , Guarve Kumar 
Published: October 2022

Background:
Colorectal cancer (CRC) is the third most widely spread tumor among the
human population. It is usually adenocarcinomatous and develops as a polyp on the inner wall of
the colon or rectum which may become malignant with time. Though its treatment is limited, its
early diagnosis and prevention play a better role, thereby decreasing mortality rates.

Objective:
The molecular markers in CRC-affected tissues may play an important role to develop
novel strategies to cure the disease. Nanotechnology consists of both an innovative diagnostic and
therapeutic array of nanomaterials that may be used to target CRC like dendrimers, carbon nanotubes,
nanoparticles, nano-emulsions, etc.

Methods:
Current patents and research covering the nanotechnology used to target and diagnose
CRC is included in the review.

Results:
Nanotechnology is playing a wonderful role in both the treatment and diagnosis of CRC.

Conclusion:
The present review may cover the recent advancements in nanotechnology in the
treatment and diagnosis of CRC.
The Undervalued Acute Leukopenia Induced By Radiotherapy In Cervical
Cancer
Authors: Ma Ruishuang , Guo Jianxin , Ye Xiaoxian , Zhou Jianliang , Guo Shenchao , Lou Pengrong 
Published: October 2022

Background:
Myelosuppression is common and threatening during tumor treatment.
However, the effect of radiation on bone marrow activity, especially leukocyte count, has been
underestimated in cervical cancer. The aim of this study was to evaluate the severity of radiotherapy-
induced acute leukopenia and its relationship with intestinal toxicity.

Methods:
The clinical data of 59 patients who underwent conventional radiation alone for cervical
cancer were retrospectively analyzed. The patients had normal leukocyte count on admission, and
the blood cell count, gross tumor volume (GTV) dose, and intestinal toxicity were evaluated.

Results:
During radiotherapy (RT), 47 patients (79.7%) developed into leukopenia, with 38.3%
mild and 61.7% moderate. The mean time for leukopenia was 9 days. Compared with leukopenianegative
patients, leukopenia-positive ones had lower baseline leukocyte count, while neutrophil/
lymphocyte (NLR) and monocyte/lymphocyte (MLR) showed no significance. Logistic regression
analysis indicated that excluding the factors for age, body mass index (BMI), TNM stage,
surgery and GTV dose, baseline leukocyte count was an important independent predictor of leukopenia
(OR=0.383). During RT, a significant reduction was found in leukocyte, neutrophil and
lymphocyte count at week 2 while monocyte count after 2 weeks. Furthermore, NLR and MLR
showed a significant and sustained upward trend. About 54.2% of patients had gastrointestinal
symptoms. However, no significant relevance was noted between leukocyte count as well as
NLR/MLR and intestinal toxicity, indicating leukopenia may not be the main factor causing and
aggravating gastrointestinal reaction in cervical cancer.

Conclusion:
Our results suggest the underrated high prevalence and severity of leukopenia in cervical
cancer patients receiving RT, and those with low baseline leukocyte count are more likely for
leukopenia, for whom early prevention of infection may be needed during RT.
Artificial Intelligence–Driven Serious Games in Health Care: Scoping Review
Authors: Abd-alrazaq Alaa , Abuelezz Israa , Hassan Asma , AlSammarraie AlHasan , Alhuwail Dari , Irshaidat Sara , Abu Serhan Hashem , Ahmed Arfan , Alabed Alrazak Sadam , Househ Mowafa 
Published: October 2022

Background
Artificial intelligence (AI)–driven serious games have been used in health care to offer a customizable and immersive experience. Summarizing the features of the current AI-driven serious games is very important to explore how they have been developed and used and their current state to plan on how to leverage them in the current and future health care needs.

Objective
This study aimed to explore the features of AI-driven serious games in health care as reported by previous research.

Methods
We conducted a scoping review to achieve the abovementioned objective. The most popular databases in the information technology and health fields (ie, MEDLINE, PsycInfo, Embase, CINAHL, IEEE Xplore, ACM Digital Library, and Google Scholar) were searched using keywords related to serious games and AI. Two reviewers independently performed the study selection process. Three reviewers independently extracted data from the included studies. A narrative approach was used for data synthesis.

Results
The search process returned 1470 records. Of these 1470 records, 46 (31.29%) met all eligibility criteria. A total of 64 different serious games were found in the included studies. Motor impairment was the most common health condition targeted by these serious games. Serious games were used for rehabilitation in most of the studies. The most common genres of serious games were role-playing games, puzzle games, and platform games. Unity was the most prominent game engine used to develop serious games. PCs were the most common platform used to play serious games. The most common algorithm used in the included studies was support vector machine. The most common purposes of AI were the detection of disease and the evaluation of user performance. The size of the data set ranged from 36 to 795,600. The most common validation techniques used in the included studies were k-fold cross-validation and training-test split validation. Accuracy was the most commonly used metric for evaluating the performance of AI models.

Conclusions
The last decade witnessed an increase in the development of AI-driven serious games for health care purposes, targeting various health conditions, and leveraging multiple AI algorithms; this rising trend is expected to continue for years to come. Although the evidence uncovered in this study shows promising applications of AI-driven serious games, larger and more rigorous, diverse, and robust studies may be needed to examine the efficacy and effectiveness of AI-driven serious games in different populations with different health conditions.
Kisceral Argumentation in Law
Authors: Novak Marko 
Published: October 2022

Gilbert's kisceral argumentation is, roughly speaking, about arguing based on intuitions. In the forefront of such a (rhetorical) model are arguers and audiences, who resolve disagreements using kisceral arguments. Intuitions as reasons were more important in pre-modern law, when the law was not as explicit, precise, and determinate as today. Law influenced by religion or religious law was a typical example. In our much more secular modern era, intuitions are more or less subordinated to the (legal) logical mode of arguing. However, in tough legal cases, when logic "runs out," it is values that decide them. Not surprisingly, neuroscience and cognitive psychology have shown a strong connection between values and intuition.
Success rate, treatment duration, and pain perception in the management of palatally impacted canines using the K9 and Ballista spring: a randomized clinical trial
Authors: Verma Sanjeev , Singh Satinder Pal , Verma Raj Kumar , Kumar Vinay , Singh Sombir , Bhupali Nameksh Raj 
Published: October 2022

ABSTRACT

Objectives
To evaluate the success rate, treatment duration, and pain perceived during forced eruption of maxillary palatally impacted canines using the K9 and Ballista springs.

Materials and Methods
Thirty unilateral palatal canine impactions of moderate and difficult category as determined by KPG index (score between 10 and 19) were enrolled. Group 1 comprised canine impactions managed with K9 springs, and Group 2 comprised Ballista springs. Block randomization and opaque sealed envelopes were used for allocation. The success rate and treatment duration (application of force to ligation of the impacted canine into the initial alignment archwire) were recorded. Pain perception was evaluated on a 10-point visual analogue scale (VAS) and modified McGill Pain Questionnaire. Chi-square test and Mann-Whitney U-test were used to compare the groups.

Results
The success rate for eruption of palatally impacted canines was 100%. The average treatment duration was 296.13 ± 96.45 days and 311.93 ± 94.34 days, respectively for Group 1 and Group 2. VAS scale scores for pain were greater for Group 2 compared to Group1, and the differences were statistically significant at all time intervals except at T1. The frequency of none and mild pain was significantly greater at all time intervals in both groups.

Conclusions
The impacted canines of moderate and difficult category were erupted with a 100% success rate and similar treatment duration with both interventions. The pain scores of Ballista springs were greater after 24 hours of force application.
Analysis of overcorrection to be included for planning clear aligner therapy: a retrospective study
Authors: Palone Mario , Pignotti Andrea , Morin Eugenia , Pancari Carolina , Spedicato Giorgio Alfredo , Cremonini Francesca , Lombardo Luca 
Published: October 2022

ABSTRACT

Objectives
To provide clinical information on overcorrection to be included in the initial digital setup to make clear aligner therapy (CAT) more efficient.

Materials and Methods
Prescription data for 150 patients (80 women and 70 men; mean age 33.7 ± 12.7 years) treated successfully with CAT (F22 Aligners, Sweden & Martina, Due Carrare, Italy) and requiring only a single, minimal finishing phase were acquired retrospectively. The inclusion criteria were Class I dental malocclusion with only minimal crowding (≤3 mm), 12–20 aligner steps per arch, no use of auxiliaries or interarch elastics, and rotations ≤25° for round-shaped teeth. The prescribed and corrective movements to be achieved in the main and finishing treatment phases, respectively, were quantified by the dedicated clear aligner setup software. The magnitudes of inclination (buccal-lingual crown tipping), angulation (mesial-distal crown tipping), rotation, intrusion, and extrusion were extracted and analyzed by tooth type, maxilla and mandible, and both arches. Descriptive statistics, that is, mean, standard deviation, and percentage, were calculated for each movement investigated. Classification and regression trees (CART) were generated using the model-based recursive partitioning approach, and the corrective movements were correlated with respect to both the amount of the movements prescribed and the tooth type. Statistical significance was set at 5%.

Results
Inclination and rotation required the greatest correction, whereas angulation, intrusion, and extrusion required only minimal correction. Expressed as a percentage of prescribed movement, mean corrective movements were 20.5% for inclination, 14.5% angulation, 28.4% rotation, 11.7% extrusion, and 22% intrusion. According to CART, all corrective movements except extrusion depended on both tooth type and the magnitude of prescribed movement.

Conclusions
To achieve more efficient CAT, approximately 20% overcorrection should be added to the initial planning phase when planning challenging movements such as inclination and rotation.
Study on Transmission Characteristics in Three Kinds of Deformed Finlines Based on Edge-Based Finite Element Method
Authors: Sun Hai , Elzefzafy Hend 
Published: October 2022

Abstract
The influence of model deformation on the transmission characteristics in unilateral finline, antipodal finline and bilateral finline is discussed using edge-based finite element method (FEM). The deformation is considered with respect to the eight boundaries of the model, and their amplitude is set to 0.01–0.05; the transmission characteristics include the cutoff wavelength characteristics of the dominant mode and the single-mode bandwidth characteristics. The results show a decreasing trend when the vacuum area is deformed, while they show a variety of possibilities when the loading region is deformed. These numerical results have strong guiding significance for the influence of the deformation of finlines on the overall device.
Prediction of corporate financial distress based on digital signal processing and multiple regression analysis
Authors: Li Liyang , Yousif Mohammed , El-Kanj Nasser 
Published: October 2022

Abstract
In order to reduce the default rate of corporate bond market, the author proposes to use digital signal processing and multiple regression analysis to study the prediction system of financial distressed companies. First, design the research method, Logistic regression model is the most commonly used multivariate statistical method when modeling binary dependent variables, it can solve the problem of nonlinear classification, it has no specific requirements for the distribution of variables, and the accuracy of judgment is high. The author selects 32 financial ratios from the perspectives of solvency, operating ability, profitability, development ability, per share index, and risk level. Taking special treatment (ST) due to abnormal financial status as a sign of financial distress in listed companies, when selecting samples, the matching principle is adopted to select non-ST companies as matching samples. Two methods of logistic regression and support vector machine are used for empirical testing, and both in-sample testing and out-of-sample prediction are performed. The results show that when using the logistic regression method, the propensity to default indicator (TTD) reflected in the text content, it can indeed improve the out-of-sample prediction accuracy of the financial distress prediction model, and it is consistent with the in-sample test, this is mainly reflected in the reduction of the first type of error, that is, the probability of misjudging a financially distressed company as a normal company. Changes in the proportions have little effect on the relative importance of financial ratio variables when modeling with support vector machines, the propensity to default indicator (TTD) entered the top ten important variables in both ratios, and ranked fourth among all indicators when the ratio was 1:2, importance has increased significantly. From this it can be seen that, when using support vector machine to build a financial distress prediction model, the propensity to default indicator (TTD) has played an important role. In the case of using the support vector machine method, adding the default tendency indicator (TTD) reflected by the text information can also improve the accuracy of the financial distress prediction model.
Capacitary characterization of variable exponent Sobolev trace spaces
Authors: Berghout Mohamed 
Published: October 2022

Abstract
Let Ω ⊂ ℝn

be an open set. We give a new characterization of zero trace functions

f
∈
𝒞

(

Ω
¯

)

∩

W

0

1
,
p

(
.
)

(
Ω
)

f \in \mathcal{C}\left( {\bar \Omega } \right) \cap W_0^{1,p\left( . \right)}\left( \Omega \right)

. If in addition Ω is bounded, then we give a sufficient condition for which the mapping

f
↦

ℒ

p

(
.
)

,
f

Ω

f \mapsto \mathcal{L}_{p\left( . \right),f}^\Omega

from a set of real extended functions f : ∂Ω −→ ℝ to the nonlinear harmonic space (Ω,ℋℒ

p

(.)
) is injective, where

ℒ

p

(
.
)

,
f

Ω

\mathcal{L}_{p\left( . \right),f}^\Omega

denotes the Perron-Wiener-Brelot solution for the Dirichlet problem:

{

ℒ

p

(
.
)

u
:
=
-

Δ

p

(
.
)

u
+
ℬ

(

.
,
u

)

=
0

i
n

Ω
;

u
=
f

o
n

∂
Ω
,

\left\{ {\matrix{{{\mathcal{L}_{p\left( . \right)}}u: = - {\Delta _{p\left( . \right)}}u + \mathcal{B}\left( {.,u} \right) = 0} \hfill & {in\,\Omega ;} \hfill \cr {u = f} \hfill & {on\,\partial \Omega ,} \hfill \cr } } \right.

where ℬ is a given Carathéodory function satisfies some structural conditions.
Existence, uniqueness weak solution for a dynamic Full von Karman System of thermoelasticity
Authors: Oudaani J. 
Published: October 2022

Abstract
In this paper we study a model of full Von-Karman system coupled to the thermoelastic equations, with rotational forces, nor clamped boundary conditions. Our fundamental goal is to establish the existence as well as the uniqueness of a weak solution for the so-called global energy. As the end we displays a numerical simulation.
Promotion of the EIA Subsystem of the 3D-Quarry Web Application
Authors: Zarubina Venera , Zarubin Mikhail , Yessenkulova Zhaukhar , Salimbayeva Rassima , Satbaeva Gulbarshyn , Kenzhebekova Dina 
Published: October 2022

Abstract
The software market for computerization of modelling and design processes of mining enterprises is represented by American, Australian and Russian solutions, which increases the dependence of Kazakhstani enterprises on third-party developers. Kazakhstani cloud service, scalable according to selected solutions, allows not only to reduce dependence on exported solutions, but also to better adapt solutions to regulatory and legislative documents. At the same time, the promotion of both the service itself and its individual solutions is associated with a number of problems. A marketing strategy for promoting an EIA solution for a software product will allow you to focus on promoting both the entire cloud system and the EIA solution, conquering your market segment, and increasing competitiveness. The consequences of open pit mining may not be entirely favourable when it comes to its social and environmental impacts. This study presents a strategy for promoting a program module for environmental impact assessment (EIA) of open pit mines. The aim of the study is to develop a marketing strategy for promoting the EIA subsystem of the 3DCAREER web application in the context of the globalization of the world economy. Promotion of the module for environmental assessment of the impact of open pits on the extraction of common minerals on the environment to optimize calculations for assessing the impact of open pits in dynamically changing environmental conditions of the enterprise will minimize negative environmental impacts.