bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimized Synthesis and Cytotoxic Activity of α-Aminophosphonates
Against a Multidrug Resistant Uterine Sarcoma Cell Line
Authors: Tóth Szilárd , Keglevich György , Varga Petra Regina , Dinnyési Emőke , Szakács Gergely 
Published: June 2022

Background:
α-Aminophosphonates are potentially biologically active species.

Objective:
We wished to compare the synthetic methods and evaluate the effect of the α-
aminophosphonates on sarcoma cell lines.

Methods:
We investigated microwave-assisted Kabachnik–Fields and Pudovik reactions, as well as substitutions,
and applied in vitro cytotoxicity screening.

Results:
The Kabachnik–Fields condensation and the Pudovik reaction were found to be the most suitable
regarding efficiency. Surprisingly, the multidrug resistant (MDR) uterine sarcoma (Mes-Sa/Dx5) cell line
was the most susceptible to the aminophosphonates tested.

Conclusions:
α-Aminophosphonates may indeed display anticancer effect. Substituents in the para position
of the phenyl ring have an impact on the activity: the 4-Me and 4-Cl derivatives were more toxic to
all cell lines as compared to the 4-H and 4-MeO species.
A Deep Learning Model for Diagnosing COVID-19 and Pneumonia
through X-ray
Authors: Liu Xiangbin , Wu Wenqian , Chun-Wei Lin Jerry , Liu Shuai 
Published: June 2022

Background:
The new global pandemic caused by the 2019 novel coronavirus (COVID-19),
novel coronavirus pneumonia, has spread rapidly around the world, causing enormous damage to daily
life, public health security, and the global economy. Early detection and treatment of COVID-19 infected
patients are critical to prevent the further spread of the epidemic. However, existing detection
methods are unable to rapidly detect COVID-19 patients, so infected individuals are not detected in a
timely manner, which complicates the prevention and control of COVID-19 to some extent. Therefore,
it is crucial to develop a rapid and practical COVID-19 detection method. In this work, we explored the
application of deep learning in COVID-19 detection to develop a rapid COVID-19 detection method.

Method:
Existing studies have shown that novel coronavirus pneumonia has significant radiographic
performance. In this study, we analyze and select the features of chest radiographs. We propose a chest
X-Ray (CXR) classification method based on the selected features and investigate the application of
transfer learning in detecting pneumonia and COVID-19. Furthermore, we combine the proposed CXR
classification method based on selected features with transfer learning and ensemble learning and propose
an ensemble deep learning model based on transfer learning called COVID-ensemble to diagnose
pneumonia and COVID-19 using chest x-ray images. The model aims to provide an accurate diagnosis
for binary classification (no finding/pneumonia) and multivariate classification (COVID-19/No findings/
Pneumonia).

Results:
Our proposed CXR classification method based on selection features can significantly improve
the CXR classification accuracy of the CNN model. Using this method, DarkNet19 improved its
binary and triple classification accuracies by 3.5% and 5.78%, respectively. In addition, the COVIDensemble
achieved 91.5% accuracy in the binary classification task and 91.11% in the multi-category
classification task. The experimental results demonstrate that the COVID-ensemble can quickly and
accurately detect COVID-19 and pneumonia automatically through X-ray images and that the performance
of this model is superior to that of several existing methods.

Conclusion:
Our proposed COVID-ensemble can not only overcome the limitations of the conventional
COVID-19 detection method RT-PCR and provide convenient and fast COVID-19 detection but
also automatically detect pneumonia, thereby reducing the pressure on the medical staff. Using deep
learning models to automatically diagnose COVID-19 and pneumonia from X-ray images can serve as
a fast and efficient screening method for COVID-19 and pneumonia.
A Short Review on the Efficacy of Derivatives of Curcumin
Authors: Sameera Rege , Varshneya Megha , Momin Shamim 
Published: June 2022

Abstract:
Curcumin with medicinal value should possess good bioavailability and stability. Unfortunately,
the bioavailability of curcumin is less, and its stability depends on the medium. After intake
of curcumin, it undergoes metabolism to form metabolites, which are bioactive and show better bioavailability
than curcumin. Hence, the disadvantages of curcumin can be overcome by the formation
of its synthetic derivative. In this review paper, the derivatives of curcumin, namely tetrahydrocurcumin,
hexahydrocurcumin, octahydrocurcumin, deketene curcumin, and dimethoxycurcumin are
discussed. The diketo group, diene moiety, and ortho-methoxy phenolic groups present in curcumin
influence its properties. Curcumin undergoes degradation in alkaline and non-polar mediums, mainly
because of its property to exhibit keto-enol tautomerism, which is further enhanced due to the presence
of diene moiety and ortho-methoxy phenolic groups. If the keto-enol tautomerism is inhibited
by its derivatization, then its stability is improved. This is especially true in the case of hexahydrocurcumin,
octahydrocurcumin, and deketene curcumin, which do not possess an active methylene
group. Although tetrahydrocurcumin undergoes keto-enol tautomerism, the degradation of enol tautomer
is prevented due to the absence of diene moiety. The activities of curcumin are medium dependent,
whereas the activities of hydrogenated derivatives of curcumin and deketene curcumin are
medium independent. Dimethoxycurcumin exhibits keto-enol tautomerism and can undergo degradation
in alkaline and non-polar mediums at a faster rate than curcumin. The reason is that the strong
activating hydroxyl groups in curcumin are replaced by a stronger activating methoxy group in dimethoxycurcumin.
On the other hand, in acidic and polar mediums, the rate of beneficial activities
of dimethoxycurcumin is more than curcumin. Also, the bioavailability of the above-mentioned derivatives
of curcumin is more than curcumin.
The Psychological Sequelae of COVID-19 Among Disease Survivors
in Argentina
Authors: Tisocco Franco , Doolan Alejandra , Vicente María Laura , Liporace Mercedes Fernández , Solano Alejandro Castro 
Published: June 2022

Background:
While more than a year has passed since the COVID-19 outbreak, it is
still a growing health concern. Moreover, ample consensus exists for the presence of not only a
physical but also a psychological impact of the COVID-19 pandemic. Those reported as hardest
hit were individuals who had been infected with COVID-19. Survivors have exhibited a
higher prevalence of psychological morbidity i.e., PTSD, depression, and anxiety- as compared
with the general population and health workers. Additionally, COVID-19 patients and survivors
have been psychologically impacted by a staggering number of disease-related stressors.

Objective:
The study was aimed at analyzing COVID-19's impact on the psychological state of
Argentinian disease survivors.

Method:
Two hundred and ninety-six COVID-19 survivors (67.2% female; Mage = 44.81;
SDage = 12.16) from a major Buenos Aires hospital completed a questionnaire and a set of
psychological measures - COVID-19 emotional impact, psychological sequelae, disease-related
stressors, PTSD, and psychological distress.

Results:
The most impactful psychological sequelae and disease stressors revolved around having
the disease awaiting test results, fear of infecting loved ones, being apart from family and
friends during the disease, fear of physical sequelae and symptoms, and returning to isolation.
PTSD prevalence rates were 33.8%. Survivor's psychological distress levels were moderately
higher than pre-COVID-era general population levels, yet not significantly different from pre-
COVID-era clinical inpatients. Female gender, age, and hospital admission emerged as significant
predictors of increased adverse psychological outcomes.

Conclusion:
Intervention for COVID-19 survivors is urgently needed, with particular attention
to the alarming PTSD prevalence rates, as discussed in the study.
Assessment of Nano Lipid Carrier Loaded Transdermal Patch of Rizatriptan
Benzoate
Authors: Bhattacharyya Sayani , Nanjareddy Lavanya 
Published: June 2022

Background:
Migraine is a neurological disorder and is accompanied by different painful
episodes. Hence, the maintenance of a steady-state concentration of drugs can be beneficial for
patients suffering from migraine. The present investigation focuses on the development of nano
lipid carriers (NLCs) loaded with transdermal patches of rizatriptan benzoate to sustain the effect
of the drug for the enhancement of therapeutic effects.

Method:
Stearic acid and peanut oil were used to make the NLCs. A central composite design
was employed to observe the effect of formulation factors like solid lipid ratio, phase volume ratio,
and concentration of surfactants on the formation of nanoparticles. The effects were evaluated for
the responses like particle size and entrapment of the drug in the nanocarriers. The optimized formulation
was subjected to compatibility, thermal, surface characteristics, and surface morphology
studies. The optimized formulation was dispersed in HPMC 15CPS and PVP K30 polymer matrix,
and the transdermal patch was evaluated for its mechanical properties, drug release study, and skin
irritation study.

Results:
The experimental design was suitable to produce nanosized stable lipid carriers of the
drug with high drug entrapment. The drug and excipients were found to be compatible. The thermal
and surface characteristics study proved the high loading of drugs in the nanoparticles. The
surface morphology study showed the formation of irregular-shaped NLCs. The transdermal patch
had good mechanical properties. The ex vivo study of the formulated patch showed a sustained
release of the drug over 24h. No skin irritation was reported from the transdermal patch.

Conclusion:
Therefore, it can be concluded that the nanoparticles loaded transdermal patch of
rizatriptan benzoate can be promising in controlling the divergent phases of migraine.
Prediction of Maternal Hemorrhage Using Machine Learning: Retrospective Cohort Study
Authors: Westcott Jill M , Hughes Francine , Liu Wenke , Grivainis Mark , Hoskins Iffath , Fenyo David 
Published: June 2022

Background
Postpartum hemorrhage remains one of the largest causes of maternal morbidity and mortality in the United States.

Objective
The aim of this paper is to use machine learning techniques to identify patients at risk for postpartum hemorrhage at obstetric delivery.

Methods
Women aged 18 to 55 years delivering at a major academic center from July 2013 to October 2018 were included for analysis (N=30,867). A total of 497 variables were collected from the electronic medical record including the following: demographic information; obstetric, medical, surgical, and family history; vital signs; laboratory results; labor medication exposures; and delivery outcomes. Postpartum hemorrhage was defined as a blood loss of ≥1000 mL at the time of delivery, regardless of delivery method, with 2179 (7.1%) positive cases observed.
Supervised learning with regression-, tree-, and kernel-based machine learning methods was used to create classification models based upon training (21,606/30,867, 70%) and validation (4630/30,867, 15%) cohorts. Models were tuned using feature selection algorithms and domain knowledge. An independent test cohort (4631/30,867, 15%) determined final performance by assessing for accuracy, area under the receiver operating curve (AUROC), and sensitivity for proper classification of postpartum hemorrhage. Separate models were created using all collected data versus models limited to data available prior to the second stage of labor or at the time of decision to proceed with cesarean delivery. Additional models examined patients by mode of delivery.

Results
Gradient boosted decision trees achieved the best discrimination in the overall model. The model including all data mildly outperformed the second stage model (AUROC 0.979, 95% CI 0.971-0.986 vs AUROC 0.955, 95% CI 0.939-0.970). Optimal model accuracy was 98.1% with a sensitivity of 0.763 for positive prediction of postpartum hemorrhage. The second stage model achieved an accuracy of 98.0% with a sensitivity of 0.737. Other selected algorithms returned models that performed with decreased discrimination. Models stratified by mode of delivery achieved good to excellent discrimination but lacked the sensitivity necessary for clinical applicability.

Conclusions
Machine learning methods can be used to identify women at risk for postpartum hemorrhage who may benefit from individualized preventative measures. Models limited to data available prior to delivery perform nearly as well as those with more complete data sets, supporting their potential utility in the clinical setting. Further work is necessary to create successful models based upon mode of delivery and to validate the findings of this study. An unbiased approach to hemorrhage risk prediction may be superior to human risk assessment and represents an area for future research.
Diagnosis of Atrial Fibrillation Using Machine Learning With Wearable Devices After Cardiac Surgery: Algorithm Development Study
Authors: Hiraoka Daisuke , Inui Tomohiko , Kawakami Eiryo , Oya Megumi , Tsuji Ayumu , Honma Koya , Kawasaki Yohei , Ozawa Yoshihito , Shiko Yuki , Ueda Hideki , Kohno Hiroki , Matsuura Kaoru , Watanabe Michiko , Yakita Yasunori , Matsumiya Goro 
Published: June 2022

Background
Some attempts have been made to detect atrial fibrillation (AF) with a wearable device equipped with photoelectric volumetric pulse wave technology, and it is expected to be applied under real clinical conditions.

Objective
This study is the second part of a 2-phase study aimed at developing a method for immediate detection of paroxysmal AF, using a wearable device with built-in photoplethysmography (PPG). The objective of this study is to develop an algorithm to immediately diagnose AF by an Apple Watch equipped with a PPG sensor that is worn by patients undergoing cardiac surgery and to use machine learning on the pulse data output from the device.

Methods
A total of 80 patients who underwent cardiac surgery at a single institution between June 2020 and March 2021 were monitored for postoperative AF, using a telemetry-monitored electrocardiogram (ECG) and an Apple Watch. AF was diagnosed by qualified physicians from telemetry-monitored ECGs and 12-lead ECGs; a diagnostic algorithm was developed using machine learning on the pulse rate data output from the Apple Watch.

Results
One of the 80 patients was excluded from the analysis due to redness caused by wearing the Apple Watch. Of 79 patients, 27 (34.2%) developed AF, and 199 events of AF including brief AF were observed. Of them, 18 events of AF lasting longer than 1 hour were observed, and cross-correlation analysis showed that pulse rate measured by Apple Watch was strongly correlated (cross-correlation functions [CCF]: 0.6-0.8) with 8 events and very strongly correlated (CCF>0.8) with 3 events. The diagnostic accuracy by machine learning was 0.9416 (sensitivity 0.909 and specificity 0.838 at the point closest to the top left) for the area under the receiver operating characteristic curve.

Conclusions
We were able to safely monitor pulse rate in patients who wore an Apple Watch after cardiac surgery. Although the pulse rate measured by the PPG sensor does not follow the heart rate recorded by telemetry-monitored ECGs in some parts, which may reduce the accuracy of AF diagnosis by machine learning, we have shown the possibility of clinical application of using only the pulse rate collected by the PPG sensor for the early detection of AF.
Promoting Physical Activity Among University Students During the COVID-19 Pandemic: Protocol for a Randomized Controlled Trial
Authors: Goncalves Aurelie , Bernal Caroline , Korchi Karim , Nogrette Maxence , Deshayes Maxime , Philippe Antony G , Gisclard Béatrice , Charbonnier Elodie 
Published: June 2022

Background
Since the beginning of the COVID-19 pandemic, sanitary context and e-learning have greatly modified student lifestyles and led to deteriorations in their mental health. An increase in anxiety and depressive symptoms and sedentary behaviors, reduction in physical activity, and a stronger tendency to move toward unhealthy diet have been demonstrated. This finding highlights the need for innovative interventions to promote healthy lifestyle among students.

Objective
This research protocol aims to evaluate the effects of an intervention program on the lifestyle and psychological state of students.

Methods
Students from University of Nîmes were recruited and randomly assigned to 1 of 2 following conditions: an intervention group and a control group. Participants in the intervention group were engaged in an 8-week physical activity program. Prior to the start of the program, design-based innovative workshops were conducted with participants to ensure that the program was co-constructed by the users and met their specific needs. Students in the control group did not receive any intervention. For each group, measures of physical activity, sedentary time, anthropometric data, sleep, physical condition, and psychological variables (eg, anxiety, depression, motivation, body appreciation, perceived control, well-being) were conducted at baseline and 9 weeks later.

Results
A total of 110 participants were initially included. Reporting of the results is projected for the spring of 2022.

Conclusions
It is anticipated that this innovative intervention co-constructed by pairs will promote a healthier lifestyle and psychological health in students. There is every reason to believe that a mobilized co-construction approach is a promising strategy to limit unhealthy habits and promote physical activity while increasing motivation. The development and evaluation of interventions to address the specific needs of university students is essential and could be transferred to other vulnerable populations such as people with chronic diseases or older people.

Trial Registration
ClinicalTrials.gov NCT05019482; https://clinicaltrials.gov/ct2/show/NCT05019482

International Registered Report Identifier (IRRID)
DERR1-10.2196/36429
Social Listening to Enhance Access to Appropriate Pandemic Information Among Culturally Diverse Populations: Case Study From Finland
Authors: Lohiniva Anna-Leena , Sibenberg Katja , Austero Sara , Skogberg Natalia 
Published: June 2022

Background
Social listening, the process of monitoring and analyzing conversations to inform communication activities, is an essential component of infodemic management. It helps inform context-specific communication strategies that are culturally acceptable and appropriate for various subpopulations. Social listening is based on the notion that target audiences themselves can best define their own information needs and messages.

Objective
This study aimed to describe the development of systematic social listening training for crisis communication and community outreach during the COVID-19 pandemic through a series of web-based workshops and to report the experiences of the workshop participants implementing the projects.

Methods
A multidisciplinary team of experts developed a series of web-based training sessions for individuals responsible for community outreach or communication among linguistically diverse populations. The participants had no previous training in systematic data collection or monitoring. This training aimed to provide participants with sufficient knowledge and skills to develop a social listening system based on their specific needs and available resources. The workshop design took into consideration the pandemic context and focused on qualitative data collection. Information on the experiences of the participants in the training was gathered based on participant feedback and their assignments and through in-depth interviews with each team.

Results
A series of 6 web-based workshops was conducted between May and September 2021. The workshops followed a systematic approach to social listening and included listening to web-based and offline sources; rapid qualitative analysis and synthesis; and developing communication recommendations, messages, and products. Follow-up meetings were organized between the workshops during which participants could share their achievements and challenges. Approximately 67% (4/6) of the participating teams established social listening systems by the end of the training. The teams tailored the knowledge provided during the training to their specific needs. As a result, the social systems developed by the teams had slightly different structures, target audiences, and aims. All resulting social listening systems followed the taught key principles of systematic social listening to collect and analyze data and used these new insights for further development of communication strategies.

Conclusions
This paper describes an infodemic management system and workflow based on qualitative inquiry and adapted to local priorities and resources. The implementation of these projects resulted in content development for targeted risk communication, addressing linguistically diverse populations. These systems can be adapted for future epidemics and pandemics.
Gambaran Stres Keluarga dalam Merawat Anggota Keluarga yang Menjalani Kemoterapi di Rumah Sakit Tingkat III Baladhika Husada Jember
Authors: Harianto Dwi , Murtaqib Murtaqib , Kushariyadi Kushariyadi 
Published: June 2022

The family role changes occur in families who care for chemotherapy patients can cause an increasing stress levels in the family. It has a major influence on improving the health of patients who are undergoing chemotherapy, because the family's ability to provide care to patients cannot be given optimally. The study aims to analyze how the description of family stress in caring for family members undergoing chemotherapy at Baladika Husada Level III Hospital Jember. The study uses a quantitative descriptive design, the sample of this study was collected by purposive sampling technique involving as many as 192 families. The respondents in this study are nuclear or main families who treat patients undergoing chemotherapy. The average age of respondents in this study was 42.40 years old. The data collected using the DASS 42 questionnaire to measure the level of stress that occurs in families. . The data analysis was carried out by observing the frequency table. The results of the study stated that 50.5% of families experience severe stress, 23.4% of families experience moderate stress, 16.1% of families experience very severe stress, 9.4% of families experience mild stress and 0.5% of families are under normal stress . The conclusions obtained from the results of the study show that families who care for patients undergoing chemotherapy experience severe stress. The results of this study are expected to be one of the reference to nurses for not only focus on patients, but also provide counseling to families who care for patients undergoing chemotherapy.