bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Online Cursive Handwritten Medical Words Recognition System for Busy Doctors in Developing Countries for Ensuring Efficient Healthcare Service Delivery
Authors: Tabassum Shaira , Rahman Md Mahmudur , Abedin Nuren , Rahman Md Moshiur , Ahmed Mostafa Taufiq , Ahmed Ashir 
Published: October 2021

Abstract
Doctors in developing countries are too busy to write digital prescriptions. Ninety-seven percent of Bangladeshi doctors write handwritten prescriptions, the majority of which lack legibility. Prescriptions are harder to read as they contain multiple languages. This paper proposes a machine learning approach to recognize doctors' handwriting to create digital prescriptions. A ‘Handwritten Medical Term Corpus’ dataset is developed containing 17,431 samples of 480 medical terms. In order to improve the recognition efficiency, this paper introduces a data augmentation technique to widen the variety and increase the sample size. A sequence of line data is extracted from the augmented images of 1,591,100 samples and fed to a Bidirectional LSTM. Data augmentation includes pattern Rotating, Shifting and Stretching (RSS). Eight different combinations are applied to evaluate the strength of the proposed method. The result shows 93.0% average accuracy (max: 94.5%, min: 92.1%) using Bidirectional LSTM and RSS data augmentation. This accuracy is 19.6% higher than the recognition result with no data expansion. The proposed handwritten recognition technology can be installed in a smartpen for busy doctors which will recognize the writings and digitize them in real-time. It is expected that the smartpen will contribute to reduce medical errors, save medical costs and ensure healthy living in developing countries.
Prediction Algorithm for ICU Mortality and Length of Stay Using Machine Learning
Authors: IWASE Shinya , Nakada Taka-aki , Shimada Tadanaga , Oami Takehiko , Shimazui Takashi , Takahashi Nozomi , Yamabe Jun , Yamao Yasuo , Kawakami Eiryo 
Published: October 2021

Abstract
Background: Machine learning can predict outcomes and determine variables contributing to precise prediction, and can thus classify patients with different risk factors of outcomes. This study aimed to investigate the predictive accuracy for mortality and length of stay in intensive care unit (ICU) patients using machine learning, and to identify the variables contributing to the precise prediction or classification of patients.Methods: Patients (n=12,747) admitted to the ICU at Chiba University Hospital were randomly assigned to the training and test cohorts. After learning using the variables on admission in the training cohort, the area under the curve (AUC) was analyzed in the test cohort to evaluate the predictive accuracy of the supervised machine learning classifiers, including random forest (RF) for outcomes (primary outcome, mortality; secondary outcome, and length of ICU stay). The rank of the variables that contributed to the machine learning prediction was confirmed, and cluster analysis of the patients with risk factors of mortality was performed to identify the important variables associated with patient outcomes.Results: Machine learning using RF revealed a high predictive value for mortality, with an AUC of 0.945. In addition, RF showed high predictive value for short and long ICU stays, with AUCs of 0.881 and 0.889, respectively. Lactate dehydrogenase (LDH) was identified as a variable contributing to the precise prediction in machine learning for both mortality and length of ICU stay. LDH was also identified as a contributing variable to classify patients into sub-populations based on different risk factors of mortality.Conclusion: The machine learning algorithm could predict mortality and length of stay in ICU patients with high accuracy. LDH was identified as a contributing variable in mortality and length of ICU stay prediction and could be used to classify patients based on mortality risk.
Screening Children at Risk for Developmental Disabilities Based on Face Landmark from Video Data of Mobile-Based Application: Preliminary Cross-Sectional Study
Authors: Hwang Sang Ho , Yu Yeonsoo , Kim Jichul , Lee Taeyeop , Park Yu Rang , Kim Hyo-Won 
Published: October 2021

Abstract
Background
Early detection and intervention of developmental disabilities (DDs) are critical for improving the long-term outcomes of the afflicted children. Mobile-based applications are easily accessible and may thus help the early identification of DDs.
Objective
We aimed to identify facial expression and head pose based on face landmark data extracted from face recording videos and to differentiate the characteristics between children with DDs and those without.
Methods
Eighty-nine children (DD, n=33; typically developing, n=56) were included in the analysis. Using the mobile-based application, we extracted facial landmarks and head poses from the recorded videos and performed Long Short-Term Memory (LSTM)-based DD classification.
Results
Stratified k-fold cross-validation showed that the average values of accuracy, precision, recall, and f1-score of the LSTM based deep learning model of DD children were 88%, 91%,72%, and 80%, respectively. Through the interpretation of prediction results using SHapley Additive exPlanations (SHAP), we confirmed that the nodding head angle variable was the most important variable. All of the top 10 variables of importance had significant differences in the distribution between children with DDs and those without (p<0.05).
Conclusions
Our results provide preliminary evidence that the deep-learning classification model using mobile-based children’s video data could be used for the early detection of children with DDs.
Low-intensity Pulsed Ultrasound Enhances the Efficacy of Bone-derived MSCs in Osteoarthritis Cartilage Repair by Regulating Autophagy-mediated Exosome Release
Authors: Xia Peng , Wang Qi , Song Jiulong , Wang Xiaoju , Wang Xinwei , Lin Qiang , Cheng Kai , Cheng Anliang , Li Xueping 
Published: October 2021

Abstract
Background: The present study explored whether low-intensity pulsed ultrasound (LIPUS) enhances the therapeutic efficacy of mesenchymal stem cells (MSCs) in osteoarthritis (OA) cartilage repair by regulating autophagy-mediated exosome release. Methods: MSCs were isolated from the rat bone marrow and treated with rapamycin, 3-methyladenine, or LIPUS. The mechanism of the LIPUS-stimulated exosome release by MSCs was analyzed by inhibiting autophagy. Additionally, the MSCs were co-cultured with OA chondrocytes and stimulated by LIPUS, with or without exosome release inhibitor intervention. The exosome release was detected through transmission electron microscopy (TEM), nanoparticle tracking analysis, and biomarker expression analysis. Autophagy was analyzed through TEM, autophagy-related gene expression analysis, and immunofluorescence analysis in vitro. Furthermore, a rat knee OA model was constructed and treated with MSCs, GW4869, and LIPUS. The cartilage repair was assessed through histopathological analysis and extracellular matrix protein expression analysis. Results: The in vitro results indicated that LIPUS promoted MSC exosome release by activating autophagy. The in vivo results demonstrated that LIPUS significantly enhanced the positive effects of MSCs on OA cartilage. These effects were significantly blocked by GW4869, an inhibitor of exosome release. Conclusion: LIPUS can enhance the therapeutic efficacy of MSCs in OA cartilage repair, and the underlying mechanism is related to the increase in autophagy-mediated exosome release
A Review of Current Publications Trend on Missing Data Imputation Over Three Decades: Direction and Future Research
Authors: adnan farah adibah , Jamaludin Khairur Rijal , Muhamad Wan Zuki Azman Wan , Miskon Suraya 
Published: October 2021

Abstract
Missing value or sometimes synonym as missing data, is an unavoidable issue when collecting data. It is uncontrollable and happen in almost any research fields. Hence, this study focused on identifying the current publications trend on missing data imputation techniques (1991- 2021) specifically in classification problems using bibliometric analysis. Most importantly, this research aims to uncover the potential missing data imputation methods. Two software were used; VOSViewer and Harzing Publish or Perish. Based on the Scopus database extracted in June 2021, the findings indicate an emerging trend in missing data imputation research to date, while there are two imputation methods that get the most attention; the random forest and the nearest neighbor methods.
Assessing climate change effects on declining groundwater levels using wavelet entropy (case study of Khorramabad city)
Authors: Hassanzadeh Reza , Komasi Mehdi , Derikvand Alireza 
Published: October 2021

Abstract
Changing global climate suggests a warmer future which may alter the hydrological cycle, affecting surface water as well as groundwater resources. The wavelet entropy (WE) criterion is a new indicator for analyzing time series fluctuations. In this study, effective factors decreasing the groundwater level in Khorramabad city during the years 2005–2018 were evaluated by the use of WE criterion. In general, it can be said that the decreasing WE criterion or time series complexity of a phenomenon shows the time series decrease of natural fluctuation, which leads to an unfavorable trend. In this regard, in order to identify the factors affecting groundwater level decrease in Khorramabad, the groundwater level was divided into 4 time periods, and after being investigated, the monthly time series of runoff, temperature, and precipitation of this city were also divided into 4 periods. Each of these subsets were decomposed into several other subsets at different time scales under the wavelet transform, and finally, after calculation of the normalized wavelet energy for this subset, the WE criterion was calculated for each period. Investigation of WE complexity shows a 21.3% decrease in groundwater level in the second period, but in the third and fourth periods, it increased by 145 and 272%, respectively. Also, according to the results of analysis of WE changes for the precipitation time series, 35.2, 32.8, and 10.06% decrease in the second, third, and fourth periods were shown, respectively. The air temperature time series complexity decreased by 26.8% only in the third time period and in the second and fourth period, it shows an increase of 29.65 and 34.7%, respectively. However, the runoff time series did not show any reduction complexity according to the WE criterion. These results indicate that the impact of climatic factors has been more effective than human factors in reducing the groundwater level of Khorramabad.
A dynamically controlled anaerobic/aerobic granular sludge reactor efficiently treats brewery/bottling wastewater
Authors: De Vleeschauwer Flinn , Caluwé Michel , Dobbeleers Thomas , Stes Hannah , Dockx Lennert , Kiekens Filip , Copot Cosmin , Dries Jan 
Published: October 2021

Abstract
This study investigated the application of a dynamic control strategy in an aerobic granular sludge (AGS) reactor treating real variable brewery/bottling wastewater. For 482 days, the anaerobic and aerobic reaction steps in a lab-scale AGS system were controlled dynamically. A pH-based control was used for the anaerobic step, and an oxygen uptake rate (OUR) based control for the aerobic step. Additionally, the effect of an elongated aerobic step, and the effect of the removal of the suspended solids from the influent, on AGS formation were also investigated. In comparison to a static operation, the dynamic operation resulted in similar reactor performance, related to effluent quality and the anaerobic dissolved organic carbon (DOC) uptake efficiency, while the organic loading rate was significantly higher. The removal of suspended solids from the influent by chemical coagulation with FeCl3 turned hybrid floccular-granular sludge into fully granular sludge. The granulation coincided with a significant increase in the abundance of the glycogen-accumulating Candidatus Competibacter and an increase in the content of gel-forming EPS to respectively around 14% and 30%. In conclusion, this study showed the successful application of a dynamic control strategy based on common and low-cost sensors for AGS treatment of industrial wastewater.
Long Non-Coding RNA GAS5 in Age-Related Diseases
Authors: Wang Yaqi , Xue Mengzhen , Xia Fangqi , Zhu Leiqi , Jia Dengke , Gao Yan , Li Luoying , Shi Yue , Li Yuanyang , Chen Silong , Xu Guangfu , Yuan Chengfu 
Published: October 2021

Abstract:
Aging refers to a natural process and a universal phenomenon in all cells, tissues,
organs, and the whole organism. Long non-coding RNAs (lncRNAs) are non-coding
RNAs with a length of 200 nucleotides. LncRNA growth arrest-specific 5 (lncRNA
GAS5) is often down-regulated in cancer. The accumulation of lncRNA GAS5 has been
found to be able to inhibit cancer growth, invasion, and metastasis while enhancing the
sensitivity of cells to chemotherapy drugs. LncRNA GAS5 can be a signaling protein,
which is specifically transcribed under different triggering conditions. Subsequently, it is
involved in signal transmission in numerous pathways as a signal node. LncRNA GAS5,
with a close relationship to multiple miRNAs, was suggested to be involved in the signaling
pathway under three action modes (i.e., signal, bait, and guidance). LncRNA GAS5
was found to be involved in different age-related diseases (e.g., rheumatoid arthritis, type
2 diabetes, atherosclerosis, osteoarthritis, osteoporosis, multiple sclerosis, cancer, etc.).
This study mainly summarized the regulatory effect exerted by lncRNA GAS5 on age-related
diseases.
Attention Action Needs to be Taken in Regard to the Dietary Intake of
Pregnant Women in Amman
Authors: Ghazzawi Hadeel Ali , Blasi Rand , Lawi Duha Abu 
Published: October 2021

Background:
pregnancy, RDA, nutrients intake, malnutrition, the second half of pregnancy, macronutrients, micronutrients,
public health issue, maternal nutrition.

Objective:
The study aimed to assess and evaluate energy and nutrients consumption among a
group of pregnant women in Amman Governorate during the second or third trimesters; in order to
compare the consumption with “Recommended Dietary Allowance” (RDA).

Methods:
A cross-sectional study was carried out in 2018 at the Ministry of Health’s (MoH) Maternal
and Children Health Centers (MCHC) in Amman to assess the dietary intake for a convenient
sample of 300 seemingly healthy pregnant women aged between 17 and 40 years-old and at ≥ 13
weeks of gestation. Maternal demographic cheracteristics, medical history, anthropometric measurements
and dietary data (three days recall) were collected.

Results:
The daily mean intake of carbohydrates exceeded the recommended intake for 45.3% of
pregnant women. Fiber intake was less than 28 g/day for 90% of them. Total fats intake (45.6 g/-
day) was lower than the RDA for 42.3% of the pregnant women. Two-thirds of the pregnant women
consumed a moderate amount of saturated fats (14.6 g/day). The mean daily intake of proteins
was 50.5±20.2g. More than two-thirds consumed protein less than the RDA; subsequently, intakes
of all essential amino acids were below RDAs. The intake of fat-soluble vitamins was lower than
RDA for vitamins D, E, and K.While, the intake of vitamin A, was higher than RDA. The intake of
minerals was lower than RDA for (calcium, fluoride, iodine, iron, zinc, and selenium), while the intakes
of sodium and copper were higher.

Conclusion:
The results showed inadequate and imbalanced dietary intake in comparison with
RDA. Maternal nutrition and diets of pregnant women in this study show alarming indicators that
may negatively affect the well-being of both pregnant women and fetal growth.
The Association between Maternal COVID-19 and the Rates of Cesarean:
A Systematic Review
Authors: Jenabi Ensiyeh , Masoumi Seyedeh Zahra , Refaei Mansoureh , Ladoni Niloofar , Shafiei Sargol 
Published: October 2021

Background:
Maternal COVID-19 infection has been reported to be associated with an
increase in the rates of C-sections. However, studies have not reported this association consistently.
Therefore, this systematic review and meta-analysis were conducted regarding this issue.

Methods:
We identified all publications up to January 2021 in databases of PubMed, Scopus, and
Web of Science. In addition, we reviewed gray literature and conference proceedings. The pooled
odds ratios (OR) and 95% confidence intervals (CI) measured from eligible studies were used as
random effect estimates of association among studies. Heterogeneity testing using I2 statistics was
performed to assess variance between the studies.

Results:
The pooled estimates of OR did not report a significant association between COVID-19
and the risk of cesarean section, respectively (OR = 2.02, 95% CI = 0. 4 to 3.64). There was low
heterogeneity among the studies reporting the risk of diseases associated with cesarean section
among children based on OR (I2=12.7%, P=0.333). The P-values for Begg’s and Eggerʼs regression
were 0.573 and 0.555, respectively.

Conclusion:
While we did not find an association between COVID-19 and increased rates of cesarean
sections, we cannot exclude a type II error; therefore, additional studies, including large cohort
studies by controlling confounding variables, should be performed in the future.
Ag-TiO2 Nanocomposites: Visible Or Solar Light Driven Plasmonic Photocatalysis?
Authors: Bach-Toledo Larissa , Peralta-Zamora Patricio G. , Prola Liziê Daniela Tentler 
Published: October 2021

Background:
The demand for photocatalytic processes assisted by solar radiation has stimulated
the upgrading of established systems, as the semiconductor modification with noble metals.

Objective:
the synthesis, characterization, and photocatalytic activity evaluation of the Ag-TiO2,
against sulfamethoxazole molecule, and investigate the significance of the plasmonic phenomenon in
Visible (450 - 1000nm) and UV-Vis (315-800 nm) radiation.

Methods:
Different nanocomposites Ag/TiO2 ratios were synthesized by the deposition of Ag nanoparticles
on the TiO2 surface by in-situ photoreduction, and then calcinated at 400°C for 2 hr. The
chemical-physical properties of the materials were examined by UV-Vis Diffuse Reflectance (UV-Vis
DR) Scanning Electronic Microscopy (SEM), Transmission Electronic Microscopy (TEM), X-Ray
Energy Dispersive Spectroscopy (EDS). The experiments were conducted in a cooled photochemical
reactor irradiated by halogen lamp (250W). The degradation of Sulfamethoxazole was monitored by
HPLC-DAD.

Results:
Although the prepared photocatalysts show an intense plasmonic band centered at 500 nm,
no photocatalytic activity was observed in the process assisted by artificial visible radiation (λ ≥ 450
nm). In processes assisted by artificial UV-Vis radiation, the photolysis rate of the model compound
(sulfamethoxazole) was higher than the photocatalytic rate, and in the absence of UV radiation, all the
reactions were inhibited. The positive effect of the presence of silver nanoparticles onto the TiO2 surface
was only evidenced in studies involving solar radiation.

Conclusion:
The results suggest the need for a balance between UV and Vis radiation to activate the
nanocomposite and perform the sulfamethoxazole degradation.
Patient-Reported Benefits and Limitations of Mobile Health Technologies for Diabetes in Pregnancy: Protocol for a Scoping Review
Authors: Sushko Katelyn , Wang Qi Rui , Tschirhart Menezes Holly , Fitzpatrick-Lewis Donna , Sherifali Diana 
Published: October 2021

Background
For women with pre-existing and gestational diabetes mellitus, pregnancy involves specialized and intensive medical care to improve maternal and infant outcomes. Medical management for patients with diabetes in pregnancy typically occurs via frequent face-to-face outpatient appointments. Barriers to face-to-face care during the COVID-19 pandemic have signaled the need for high-quality, patient-centered virtual health care modalities, such as mobile health (mHealth).

Objective
The objective of the proposed scoping review is to identify the patient-reported benefits and limitations of mHealth technology among women with diabetes in pregnancy. We also aim to determine how the women’s experiences align with the best practice standards for patient-centered communication.

Methods
Arksey and O’Malley’s framework for conducting scoping reviews with refinements by Levac et al will be used to guide the conduct of this scoping review. Relevant studies will be identified through comprehensive database searches of MEDLINE, Embase, Emcare, and PsycINFO. Following database searches, studies will be screened for eligibility at the title, abstract, and full-text level by two independent reviewers, with the inclusion of a third reviewer if required to reach consensus. Data charting of included studies will be conducted by one reviewer using a standardized data extraction form and verified independently by a second reviewer. Synthesis of results will be guided by Thomas and Harden’s “Methods for the Thematic Synthesis of Qualitative Research in Systematic Reviews.”

Results
As of August 2020, we have carried out the qualitative searches in the electronic databases MEDLINE, Embase, Emcare, and PsycINFO (Ovid interface) for a combined total of 8207 articles. Next, we plan to conduct the quantitative searches in the electronic databases MEDLINE, Embase, and Emcare (Ovid interface). We also plan to review the reference lists of relevant studies to identify additional eligible studies.

Conclusions
With the results of this review, we hope to describe the patient-reported benefits and limitations of mHealth technology for women with diabetes in pregnancy. Furthermore, we aim to determine how women’s experiences align with the best practice standards for patient-centered communication. Ultimately, our review can provide valuable information for guideline developers, policy makers, and clinicians related to mobile technologies to support virtual care delivery for women with diabetes in pregnancy.

International Registered Report Identifier (IRRID)
PRR1-10.2196/29727