bluuebunny/crossref_metadata_2025 huggingface dataset:
T-Shaped Functional End to End Anastomosis as an Alternative Method for Esophagojejunostomy after Totally Laparoscopic Total Gastrectomy for Gastric Cancer
Authors: Long Vo Duy , Dat Tran Quang , Thong Dang Quang , Nguyen Doan Thuy , Vu Tran Minh , Hai Nguyen Viet , Bac Nguyen Hoang
Published: March 2024

Background: The functional end-to-end anastomosis (FEEA) and overlap methods for reconstruction after totally laparoscopic total gastrectomy (TLTG) are technically challenges. We developed a T-shaped FEEA to facilitate technique. This study aimed to compare the short- and long-term outcomes between the T-shaped FEEA and overlap methods after TLTG for gastric cancer (GC).
Method: This cohort study enrolled 84 patients who underwent TLTG for GC using linear stapler for reconstruction between August-2015 and October-2020. Among them, 40 patients underwent FEEA, while 44 patients underwent overlap method. Short-term outcomes encompassed operative characteristics, postoperative morbidity, and mortality. Long-term outcomes included anastomosis-related complications and 1-year nutritional status. Oncologic outcomes were assessed regarding 1-, 3-, and 5- year overall survival (OS) and disease-free survival (DFS).
Results: There were no significant differences between the T-shaped FEEA and overlap methods in terms of the early surgical outcomes such as operating time, time of performing anastomosis, time of the first flatus, overall morbidities and mortality, and postoperative hospital stays. The esophagojejunostomy leakage and stenosis rates were similar between the groups (5.0% and 2.6% vs. 6.8% and 0.0%, respectively). The changing of reduction in weight, hemoglobin level, and albumin after 1-year postoperation were equivalent. Five-year OS and DFS were comparable between the two groups (70% and 57% vs. 51% and 41%, respectively).
Conclusions: There was no significant difference in surgical outcomes and long-term survival between the modified FEEA and overlap methods. The T-shaped FEEA may be an alternative method for reconstruction after TLTG for GC.
Correlation between body size and stone composition in pediatric stone patients
Authors: An Lizhe , Yu Luping , Xiong Liulin , Huang Xiaobo , Xu Tao , Xu Qingquan
Published: March 2024

Purpose
Due to the lack of stone composition data, the relationship between BMI and stone composition in children is rarely reported. Our study encompassed more cases than previous studies, aim to examine the correlation between BMI and stone composition in pediatric stone patients.
Methods
Clinical date of pediatric patients who underwent minimally invasive surgery for urolithiasis from 2009 to 2019 were retrospectively reviewed. Patient's gender, age, BMI, stone location, stone size, stone composition, serum uric acid, urine culture, urine pH and other clinical data were collected. According to BMI, children were divided into overweight/obese group and normal group, stone composition and other related indicators were compared between the two groups.
Results
A total of 71 children were included in the study, consisting 49 males and 22 females, with an average age of 4.9 ± 3.87 years. The overweight/obese group included 16 children (22.5%) and the normal group included 55 children (77.5%). Kidney stone was seen in 49 cases (69%), ureteral stone in 17 cases (24%) and bladder/urethral stone in 5 cases (17%). Twenty-seven cases (38%) had pure stones, and 44 cases (62%) had mixed stones. Among the main stone composition, calcium oxalate was the most common (41/71, 57.7%), followed by ammonium urate (10/71, 14.1%), carbonate apatite (6/71, 8.5%) and cystine (6/71, 8.5%). There was no statistical difference in the main stone composition between the two groups. Also, no significant difference was seen in gender, age, stone location, stone size, and urine culture positive rate. However, the overweight/obese group had higher serum uric acid (361 ± 139.3umol/L vs 271 ± 140.3umol/L, p = 0.027) and lower urine pH (6.1 ± 0.63 vs 6.4 ± 0.57, p = 0.048) than the normal group.
Conclusion
Our study showed that there was no apparent correlation between BMI and stone composition in pediatric stone patients. But overweight or obese children had higher serum uric acid and lower urine pH.
Automated Fruit Identification using Modified AlexNet Feature Extraction based FSSATM Classifier
Authors: Thirumalraj Mrs Arunadevi , Rajalakshmi B. , Kumar B Santosh , Stephe S.
Published: March 2024

Because fruits are complex, automating their identification is a constant challenge. Manual fruit categorisation is a difficult task since fruit types and subtypes are often location-dependent. A sum of recent publications has classified the Fruit-360 dataset using methods based on Convolutional Neural Networks (e.g., VGG16, Inception V3, MobileNet, and ResNet18). Unfortunately, out of all 131 fruit classifications, none of them are extensive enough to be used. Furthermore, these models did not have the optimum computational efficiency. Here we propose a new, robust, and all-encompassing research that identifies and predicts the whole Fruit-360 dataset, which consists of 90,483 sample photos and 131 fruit classifications. The research gap was successfully filled using an algorithm that is based on the Modified AlexNet with an efficient classifier. The input photos are processed by the modified AlexNet, which uses the Golden jackal optimisation algorithm (GJOA) to choose the best tuning of the feature extraction technique. Lastly, the classifier employed is Fruit Shift Self Attention Transform Mechanism (FSSATM). This transform mechanism is aimed to improve the transformer's accuracy and comprises a spatial feature extraction module (SFE) besides spatial position encoding (SPE). Iterations and a confusion matrix were used to validate the algorithm. The outcomes prove that the suggested tactic yields a relative accuracy of 98%. Furthermore, state-of-the-art procedures for the drive were located in the literature and compared to the built system. By comparing the results, it is clear that the newly created algorithm is capable of efficiently processing the whole Fruit-360 dataset.
The Relationship Between Physical Activity and Depressive Symptoms
Authors: Fu Huaying , Guo Siqiang , Guo Kelei
Published: March 2024

Purpose: To explore the relationship between physical activity and depression among college students and the mediating role of coping styles and loneliness in the relationship.
Methods: A stratified whole group sampling method was used to survey 1112 college students using the Physical Activity Scale, Depression Scale, Coping Styles Scale, and Loneliness Scale.
Results: (1) Physical activity was significantly negatively correlated with depression (r =-0.51), and the direct path of physical activity on depression was significant (β= -0.21, t= -8.418 , p＜0.01), (2) Physical activity positively predicted coping styles (β= 0.39，t= 14.202，p＜0.01) and negatively predicted loneliness (β= -0.40，t= -15.039，p＜0.01), and Coping styles significantly and negatively predicted depression (β= -0.28，t= -10.281，p＜0.01) and loneliness (β= -0.41，t= -17.187，p＜0.01), and Loneliness significantly and positively predicted depression (β= 0.26，t= 10.289，p＜0.01).
Conclusion: Physical activity can not only directly affect depression, but also indirectly through the separate mediating effects of coping styles and loneliness, as well as the chain mediating effects of the two.
Systematic Review and Meta-analysis of the Pattern, Distribution, Etiology, and Treatment of Maxillofacial Injuries From Different Geopolitical Zones in Nigeria
Authors: Adebayo Ezekiel T. , Fomete Benjamin , Egbunah Uchenna P. , Aladelusi Timothy O. , Adekunle Adegbayi A. , Adeyemo Wasiu L.
Published: March 2024

Purpose: This study aimed to provide a nationwide survey of maxillofacial injuries pattern, distribution, etiology, and treatment based on Nigeria’s geopolitical zones.
Methods: This systematic review and meta-analysis included all cross-sectional, cohort, case-control and randomized controlled studies published between 2004 and 2019 (16 years). Comparisons were made between different age groups, gender, site of maxillofacial injury, associated injuries, and treatment across geopolitical zones in Nigeria: southwest(SW), southsouth(SS), southeast(SE), northwest(NW), north central(NC) and northeast(NE). Proportion meta-analysis using the generic inverse variance method, risk ratio effect measure, and random effects model was conducted.
Results: A total of 35 studies including 4,472 patients from four geopolitical zones across Nigeria (SW, SS, SE and NW) were included in this review. Age range was 0.3 to 90 years. Highest male to female ratio reported was 30:1. Road traffic crash (RTC) was the major risk factor for maxillofacial injuries (3.10, 95% CI, 2.34 – 4.10), and motorcycle crash was second (0.44; 95% CI, 0.29 – 0.66). Other aetiologic factors were assault (0.28; 95% CI, 0.19 – 0.40), fall (0.06; 95% CI, 0.03 – 0.11), sports injuries (0.03; 95% CI, 0.02 – 0.05) and others (0.04; 95% CI, 0.02 – 0.09).
Conclusions and relevance: RTC, motorcycle crash and assault constituted the most important factors for maxillofacial injuries in Nigeria. The result of this study highlights the importance of improved road safety measures and maintenance of law and order.
„Vom Schieben und geschoben werden…“ Grundlegungen zu einer multiperspektivischen Qualifizierung (mpQ) zur Förderung multiprofessioneller Teamarbeit (mpT) im Kontext inklusiver Bildung
Authors: Kückmann Marie-Ann
Published: March 2024

Wiederholt wird im Kontext inklusiver Bildung auf die Relevanz multiprofessioneller Teamarbeit verwiesen und darauf aufbauend werden auch zunehmend angemessene Qualifizierungskon­zepte für pädagogische Fachkräfte eingefordert. Bei der Sichtung bestehender Angebote fällt dann aber auf, dass diese den Blick häufig primär auf Erfolgsfaktoren für die Teamentwicklung und damit die vorzufindenden kollektiven Strukturen richten, wodurch die Gefahr besteht, dass die individuellen, berufsbiografischen Perspektiven im Prozess entsprechend wenig beleuchtet werden oder aber ganz unberücksichtigt bleiben. Auch Einblicke in den Forschungsstand veran­schaulichen die verbundene Komplexität und heben dabei teils auch bereits individuelle, inter­aktionelle, institutionelle und gesellschaftliche Perspektiven hervor. Dabei wird jedoch nicht immer klar expliziert, welche individuellen, kollektiven oder verbindenden Verständnisse sozia­len Handelns der Forschungsarbeit jeweils zugrunde liegen. Diesbezüglich fehlen u. U. Refe­renzsysteme, die die Komplexität des sozialen Konstrukts multiprofessioneller Teamarbeit im Spannungsfeld von sowohl Struktur- als auch Handlungsmomenten aufnehmen und entspre­chenden Qualifizierungskonzepten überhaupt zugrunde gelegt werden können. Der vorliegende Artikel widmet sich diesem Anspruch, versucht diesbezüglich sowohl aus theoretischer als auch aus empirischer Perspektive vertiefende Einsichten in die multiprofessionelle Teamarbeit im Kontext inklusiver Bildung zu eröffnen, hierauf aufbauend erste Anschlussstellen für mögliche Qualifizierungskonzepte auszuweisen und soll so einen Ausgangspunkt für eine wissenschaft­lich fundierte Gestaltungsarbeit anbieten.
Abstract
The relevance of multi-professional teamwork is often mentioned in the context of inclusive education. The need for qualification concepts for teaching staff is also increasing. However, existing qualifications often focus primarily on collective structures. This involves the risk of re­ducing the content and not taking individual, professional biographical perspectives into account sufficiently. Insights into current research illustrate the associated complexity and, in some cases, also emphasise individual, interactional, institutional and social perspectives. However, it is not always clear which individual and/or collective understandings of social action underpin the research. In this sense, there is a lack of reference systems that address the complexity of the social construct of multi-professional teamwork integrating both structural as well as individ­ual perspectives. The aim of this paper is to analyse what constitutes multi-professional team­work and how it is related to the individual development of its members. Based on an extensive examination of its theoretical as well as empirical underpinnings, it lays the foundation for an ongoing productive discussion about suitable qualification concepts in this context.
Grapevines to Hydrogen Cyanamide
Authors: Dokoozlian N.K. , Ebisuda N.C. , Neja R.A.
Published: March 2024

The effects of surfactants on the efficacy of hydrogen cyanamide (H2CN2) applied to `Perlette' grapevines (Vitis vinifera L.) grown in the Coachella Valley of California were examined in 1994 and 1995. Vines were pruned in mid-December in both years and treatments applied at 1000 L·ha-1 the following day to dormant spurs and cordons using a hand-held spray wand. In 1994, H2CN2 was applied at 0.5%, 1%, or 2% by volume in combination with 0%, 0.5%, 1%, 2%, or 3% by volume of the amine-based surfactant Armobreak. In 1995, H2CN2 was applied at 0.5%, 1%, or 2% by volume in combination with Armobreak at 0% or 2% by volume. In 1994, budbreak rate was highly dependent upon H2CN2 concentration when 0% to 1% Armobreak was used; budbreak was generally most rapid for vines treated with 2% H2CN2 and slowest for vines treated with 0.5% H2CN2. When 2% or 3% Armobreak was used, however, little effect of H2CN2 concentration was observed. Results were similar in 1995, but the budbreak of vines treated with 2% H2CN2 + 2% Armobreak lagged behind that of vines treated with 1% H2CN2 + 2% Armobreak. The number of days after treatment required for 70% bud-break generally declined as the concentrations of H2CN2 and Armobreak were increased. A separate experiment conducted in 1995 revealed that several surfactants varying in chemical composition, Armobreak, Activator 90 and Agridex, had similar effects on H2CN2 efficacy. The results indicate that the addition of surfactants to H2CN2 solutions can significantly reduce the amount of active ingredient necessary for maximum efficacy on grapevines. Chemical names used: hydroxypolyoxyethylene polyoxypropylene ethyl alkylamine (Armobreak); alkyl polyoxyethylene ether (Activator 90); paraffin petroleum oil (Agridex).
Black Girls Youth Participatory Action Research & Pedagogies
Authors: Crenshaw Kimberlé , Evans-Winters Venus
Published: March 2024

Introduction to thematic issue.
ORAL TISSUE ENGINEERING
Authors: Singh Mandeep , Singh Sanjeet , Singh Nishant , Singh Paramjit , Sharma Kanika , Grover Neeraj
Published: March 2024

Oral tissue engineering is a progressive field aiming to regenerate damaged oral tissues, such as bone, gums, and salivary glands, by leveraging a combination of scaffolds, cells, and bioactive molecules. This multidisciplinary approach integrates principles from biology, materials science, and engineering to develop functional replacements for lost or injured oral tissues. Recent advancements have focused on optimizing scaffold materials to mimic the natural oral environment, identifying suitable cell sources for regeneration, and applying growth factors to enhance tissue repair and integration. These innovations offer promising avenues for improving dental and craniofacial reconstructive treatments, significantly impacting patient care in dentistry and oral surgery.
Using statistical and machine learning approaches to describe estuarine tidal dynamics
Authors: Lauer Franziska , Kösters Frank
Published: March 2024

Estuaries are ecologically valuable regions where tidal forces move large volumes of water. To understand the ongoing physical processes in such dynamic systems, a series of estuarine monitoring stations is required. Based on the measurements, estuarine dynamics can be described by key values, so-called tidal characteristics. The reconstruction and prediction of tidal characteristics by suitable approaches is essential to discover natural or anthropogenic changes. Therefore, it is of interest to inter- and extrapolate measured values in time and to investigate the spatial relationship between different stations. Normally, such system analyses are performed by deterministic numerical models. However, to facilitate long-term investigations also, statistical and machine learning approaches are good options. For a Weser estuary case study, we implemented three approaches (linear, non-linear, and artificial neural network regression) with the same database to enable the prediction of tidal extrema. Thereby we achieve an accuracy of 0.4–2.5% derivation (based on the RMSEs) while approximating measured values over 19 years. This proves that the approaches can be used for hindcast studies as well as for future analysis of system changes. Our work can be understood as a proof of concept for the practical potential of neural networks in estuarine system analysis.
Climate change risks to water security: Exploring the interplay between climate change, water theft, and water (in)security
Authors: Asaka Jeremiah Ogonda , Argomedo Daniel Weisz , Jones Nathan P.
Published: March 2024

This article explores the interplay between climate change, water theft, and water (in)security and proposes a three-node nexus conceptual framework for understanding the same. Water is essential for the proper functioning of human and natural systems. However, water scarcity that was limited to specific regions of the world in the past has today become a global crisis. The global water crisis is driven by climate change, water theft, overuse, wastage, pollution, poor governance, and population growth among other factors. This study relied on a close reading of existing scholarly literature to explore the interplay between climate change, water theft, and water (in)security. The study found that existing literature predominantly frames the interplay as a two-node nexus either between climate change and water (in)security or water theft and water (in)security. This article argues that a two-node nexus misses the interaction between climate change and water theft – a critical aspect that is crucial for a nuanced understanding of the interplay. The article proposes a three-node nexus conceptual framework for understanding the interplay. Finally, the article applies the ‘new’ framework to case studies of Kenya and Mexico and discusses insights for future policy and research.
O lugar da literatura no ensino-aprendizagem da Matemática no contexto da formação inicial docente
Authors: Souza Meire Nadja Meira de , Ferreira Ana Tereza Ramos de Jesus , Moreira Geraldo Eustáquio
Published: March 2024

Esse artigo busca investigar o lugar da literatura no ensino-aprendizagem da Matemática na formação inicial docente, com delineamento qualitativo de tipo exploratório, por meio da observação participante e utilização de questionários, fotos e vídeos. Essa investigação ocorreu no contexto de uma formação pedagógica para graduandos de Pedagogia e Matemática, com abordagem da literatura para ensinar Matemática, durante o 2º semestre letivo de 2022, na Faculdade de Educação da Universidade de Brasília. Evidenciou-se: o favorecimento da resolução de situações-problema partindo da literatura, enquanto material desafiador que instiga o pensamento matemático; a atribuição ao ensino-aprendizagem da Matemática apenas à utilização de números em detrimento da interdisciplinaridade, destacando a necessária superação  do ensino fragmentado; a importância da formação inicial do professor a partir de experiências que contribuam para o seu trabalho e prosseguimento do processo mediante a formação continuada.
Role of Gut Microbiota in Neuroinflammation and Neurological Disorders
Authors: Raj Khadga , Arora Navneet , Rohit Bentham Science Publisher , Awasthi Anupam , Patel Mayank , Chaudhary Ankit , Singh Shamsher , Gupta G.D.
Published: March 2024

The prevalence of neurological diseases such as Alzheimer’s disease (AD),
Parkinson’s disease (PD), and Multiple sclerosis (MS) are growing in the world, but
their pathogenesis is unclear and effective treatment does not exist. Neuroinflammation
is associated with many neurodegenerative mechanisms involved in neurodegenerative
diseases. The human gut microbiota is an aggregate of microorganisms that live in the
gastrointestinal tract (GIT) that plays a crucial role in maintaining human health and
the pathogenesis disease condition. The microbiota can affect neuronal function
through neurotransmitters, vitamins, and neuroactive microbial metabolites like shortchain fatty acids. The change in gut microbiota architecture causes increased
permeability of the intestine and immune system activation, contributing to systemic
inflammation, neurological injury, and eventually neurodegeneration. Available data
suggest that the microbiota send signals to the central nervous system (CNS) by
activating afferent neurons of the vagus nerve via neuroendocrine and neuroimmune
pathways. The molecular interaction between the gut/microbiome and CNS is complex
and bidirectional, ensuring gut homeostasis and proper digestion. Evidence suggests
that dysfunction of the gut-brain axis could be a significant factor leading to many
disorders of CNS. In this chapter, we explore how the gut microbiome may affect brain
function and the development of neurological disorders. In addition, we are also trying
to highlight the recent advances in improving neurological disease by supplemental
probiotics and faecal microbiota transplantation via the concept of the gut-brain axis to
combat brain-related dysfunction.
The Role of Age in Pediatric Tumors of the Central Nervous System
Authors: Kutahyalioglu Nesibe S. , Scarton Dylan V.
Published: March 2024

Pediatric tumors of the central nervous system (CNS) are the second most
common type of solid childhood cancer. As such, they have a major effect on the rates
of morbidity and mortality in children. CNS tumors originate from abnormal cells in
the brain and/or spinal cord, which can be classified as either benign or malignant.
They can be further subdivided into different categories based on several principal
aspects, such as tumor location, histopathology, and developmental age. Among these
various characteristics, age is one of the most consequential determinants for CNS
tumors. Specific groups between 0 and 21 years of age, for instance, have radically
divergent landscapes in terms of their tumor incidence and unique biology. Depending
on the age of the child, key case features may differ like the clinical evaluation,
medical diagnosis and prognosis, recommended therapy and treatment courses,
anticipated responses and tolerability to treatment, and management of side effects.
Effective teamwork is another crucial component for the successful management of
pediatric CNS tumors. In patient-and-family-centered care, ensuring a detailed
education of the children and their families, as well as their involvement in the
decision-making process where appropriate, is imperative. To determine the best
available options for the patient, multidisciplinary medical teams will often deliberate
over all of the possible procedures. The holistic care provided by these interprofessional collaborations for this vulnerable population will depend on the age of the
child, in addition to the level of patient and family participation. Evidence shows that
support and counseling of the patient and their family during the entire treatment
process can have a significant impact on outcomes. This chapter will review the
essential diagnostic and prognostic considerations of childhood CNS tumors, with
special emphasis placed on favorable therapies and treatments, including in-depth
discussions around the multi-faceted responses to treatment and the management of its
side effects. In particular, this content will highlight the critical role that age, and
interdisciplinary healthcare teams play in comprehensive disease management.
Evaluation of the stability of granulated prototypes based on a Colombian yeast strain of Meyerozyma uilliermondii and oregano essential oil
Authors: Castillo Saldarriaga Carlos
Published: March 2024

Nuevos aditivos funcionales a las dietas de rumiantes son necesarios para sostener la leche. La productividad en países tropicales como Colombia, donde el cambio climático se ha convertido en un inconveniente. En CORPOICA, se identificó una levadura colombiana nativa Meyerozyma guilliermondii cepa Lv196 como un microorganismo prometedor con un alto potencial probiótico en ensayos in vitro cuando se mezcla con aceite esencial de orégano (OEO). Debido a la propiedad antimicrobiana de OEO, una alternativa diferente de prototipos debe proponerse para evitar la pérdida de viabilidad de células de levadura durante el tiempo de almacenamiento. Por lo tanto, el objetivo de este estudio fue evaluar la estabilidad de diferentes prototipos compuesto por biomasa de levadura Lv196 y OEO para seleccionar el que garantiza la viabilidad de las células de levadura
Evaluating ChatGPT-4’s Diagnostic Accuracy: Impact of Visual Data Integration
Authors: Hirosawa Takanobu , Harada Yukinori , Tokumasu Kazuki , Ito Takahiro , Suzuki Tomoharu , Shimizu Taro
Published: March 2024

Background

In the evolving field of health care, multimodal generative artificial intelligence (AI) systems, such as ChatGPT-4 with vision (ChatGPT-4V), represent a significant advancement, as they integrate visual data with text data. This integration has the potential to revolutionize clinical diagnostics by offering more comprehensive analysis capabilities. However, the impact on diagnostic accuracy of using image data to augment ChatGPT-4 remains unclear.

Objective

This study aims to assess the impact of adding image data on ChatGPT-4’s diagnostic accuracy and provide insights into how image data integration can enhance the accuracy of multimodal AI in medical diagnostics. Specifically, this study endeavored to compare the diagnostic accuracy between ChatGPT-4V, which processed both text and image data, and its counterpart, ChatGPT-4, which only uses text data.

Methods

We identified a total of 557 case reports published in the American Journal of Case Reports from January 2022 to March 2023. After excluding cases that were nondiagnostic, pediatric, and lacking image data, we included 363 case descriptions with their final diagnoses and associated images. We compared the diagnostic accuracy of ChatGPT-4V and ChatGPT-4 without vision based on their ability to include the final diagnoses within differential diagnosis lists. Two independent physicians evaluated their accuracy, with a third resolving any discrepancies, ensuring a rigorous and objective analysis.

Results

The integration of image data into ChatGPT-4V did not significantly enhance diagnostic accuracy, showing that final diagnoses were included in the top 10 differential diagnosis lists at a rate of 85.1% (n=309), comparable to the rate of 87.9% (n=319) for the text-only version (P=.33). Notably, ChatGPT-4V’s performance in correctly identifying the top diagnosis was inferior, at 44.4% (n=161), compared with 55.9% (n=203) for the text-only version (P=.002, χ2 test). Additionally, ChatGPT-4’s self-reports showed that image data accounted for 30% of the weight in developing the differential diagnosis lists in more than half of cases.

Conclusions

Our findings reveal that currently, ChatGPT-4V predominantly relies on textual data, limiting its ability to fully use the diagnostic potential of visual information. This study underscores the need for further development of multimodal generative AI systems to effectively integrate and use clinical image data. Enhancing the diagnostic performance of such AI systems through improved multimodal data integration could significantly benefit patient care by providing more accurate and comprehensive diagnostic insights. Future research should focus on overcoming these limitations, paving the way for the practical application of advanced AI in medicine.
Investigating ChatGPT4's ability in Evidence Based Decision making in Dentistry – An Observational study (Preprint)
Authors: Shiva Shankar Bugude
Published: March 2024

BACKGROUND

Evidence-based decision-making (EBDM) is crucial in modern dentistry. Yet, navigating the vast and ever-evolving scientific literature can be daunting. Large language models (LLMs) like ChatGPT4 could revolutionize EBDM by analyzing vast data and extracting key information, significantly reducing time spent searching for evidence.

OBJECTIVE

This observational study aims to explore ChatGPT4's potential in dental EBDM, examining its capabilities, strengths, and limitations.

METHODS

Two independent dentists engaged in interactive sessions with ChatGPT4, simulating real-life clinical scenarios and seeking scientific information. ChatGPT4's responses were evaluated for accuracy, relevance, efficiency, actionability, and ethics using the ChatGPT4 Response Scoring System (CRSS) and ChatGPT4 Generative Ability Matrix (C-GAM) systems.

RESULTS

ChatGPT4 consistently performed across all five clinical scenarios, achieving a C-GAM score of 46.4% and a CRSS score of 12 out of 28. It successfully identified relevant evidence sources and provided concise summaries, potentially saving valuable time and improving access to information. However, a critical limitation was its inability to generate web links to relevant articles.

CONCLUSIONS

ChatGPT4 shows promise as an AI tool for EBDM in dentistry. Further development and training can address current limitations and enhance its effectiveness. However, clinicians retain ultimate responsibility for informed decisions, requiring expertise and critical evaluation of presented evidence.

CLINICALTRIAL

Nil
The determinants of dropping out of remote Patient-Reported Outcome (PRO)-based follow-up among patients with epilepsy: a prospective cohort study (Preprint)
Authors: Vestergaard Sofie Bech , Roost Mette , Christiansen David Høyrup , Schougaard Liv Marit Valen
Published: March 2024

BACKGROUND

Patient-reported outcome (PRO) measures comprise an emerging field in healthcare. In the Central Denmark Region, epilepsy outpatients can participate in remote PRO-based follow-up by completing a questionnaire at home instead of attending a traditional outpatient appointment. This approach aims to encourage patient engagement and is used in approximately half of all epilepsy outpatient consultations. However, drop out in this type of follow-up can be a challenge.

OBJECTIVE

This study aimed to examine the association between potential self-reported determinants and dropout in remote PRO-based follow-up for patients with epilepsy.

METHODS

This prospective cohort study (N=2282) explored the association between dropout in remote PRO-based follow-up for patients with epilepsy and nine potential determinants covering three domains: health-related self-management, general and mental health status, and patient satisfaction. The associations were examined using multiple logistic regression analyses with adjustment for gender, age, education, and cohabitation.

RESULTS

A total of 770 patients (33.7%) dropped out of remote PRO-based follow-up over 5 years. A statistically significant association between all potential determinants and dropouts in PRO-based follow-up was identified. Patients with low social support had an OR of 2.02 (95%CI:1.38;3.50) for dropout. Patients rating health as poor had an OR of 2.17 (95% CI: 1.65; 2.85) for dropout. Similar estimates were identified for the remaining determinants in question.

CONCLUSIONS

Patients with reduced self-management, poor health status, and low patient satisfaction had higher odds of dropout in remote PRO-based follow-up. Further research is needed to determine the reasons for drop out.

CLINICALTRIAL
Ensuring Appropriate Representation in Artificial Intelligence–Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias (Preprint)
Authors: O'Malley Andrew , Veenhuizen Miriam , Ahmed Ayla
Published: March 2024

BACKGROUND

In medical education, particularly in anatomy and dermatology, generative artificial intelligence (AI) can be used to create customized illustrations. However, the underrepresentation of darker skin tones in medical textbooks and elsewhere, which serve as training data for AI, poses a significant challenge in ensuring diverse and inclusive educational materials.

OBJECTIVE

This study aims to evaluate the extent of skin tone diversity in AI-generated medical images and to test whether the representation of skin tones can be improved by modifying AI prompts to better reflect the demographic makeup of the US population.

METHODS

In total, 2 standard AI models (Dall-E [OpenAI] and Midjourney [Midjourney Inc]) each generated 100 images of people with psoriasis. In addition, a custom model was developed that incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by 3 researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. A chi-square goodness of fit analysis compared the skin tone distributions from each set of images to that of the US population.

RESULTS

The standard AI models (Dalle-3 and Midjourney) demonstrated a significant difference between the expected skin tones of the US population and the observed tones in the generated images (P<.001). Both standard AI models overrepresented lighter skin. Conversely, the custom model with the modified prompt yielded a distribution of skin tones that closely matched the expected demographic representation, showing no significant difference (P=.04).

CONCLUSIONS

This study reveals a notable bias in AI-generated medical images, predominantly underrepresenting darker skin tones. This bias can be effectively addressed by modifying AI prompts to incorporate real-life demographic distributions. The findings emphasize the need for conscious efforts in AI development to ensure diverse and representative outputs, particularly in educational and medical contexts. Users of generative AI tools should be aware that these biases exist, and that similar tendencies may also exist in other types of generative AI (eg, large language models) and in other characteristics (eg, sex, gender, culture, and ethnicity). Injecting demographic data into AI prompts may effectively counteract these biases, ensuring a more accurate representation of the general population.
Maintaining Query Performance through Table Rebuilding & Archiving
Authors: Andriyani Widyastuti , Pujianto Pujianto
Published: March 2024

Despite the system previously utilizing optimal query configurations and database settings, the transaction table in the database, which is undergoing significant numerical increases and notable queries and updates on each line, has seen a drop in query speeds simultaneous with data growth. This situation arises due to an increase in disk space in the database tablespace, which results from block fragmentation. At times, database engines do not detect this problem, thereby overlooking it in the database recommendation engine. Lacking an understanding of the fundamental issue, database engineers need analysis and strategies to maintain the query speed of the transaction table in the relational database
Empirical Evidence on the Effects of Wayfair on Brick and Mortar Stores: The Case of South Dakota
Authors: Swenson Charles
Published: March 2024

Employing very specific data for South Dakota, this study shows that the requirement of collecting a sales tax by remote sellers gave rise to a 6.99% sales increase for brick-and-mortar retail sellers from 2018 through 2022. For this period there was also an approximate 4.54% increase in employment for brick-and-mortar employers. Larger operations, and those parts of a chain, experienced even higher post sales-tax growth. There was a significant amount of net moves out of the state post-2017, but employment losses for such moves were lower for brick-and-mortar retailers.
Применение технологии RedCap для оказания услуг IoT в сетях мобильной связи 5G
Authors: Тихвинский В.О. , Девяткин Е.Е. , Пастух А.С. , Савочкин А.А. , Смирнов Ю.Я.
Published: March 2024

Растущие потребности в развертывании сетей Интернета вещей на основе мобильной связи пятого поколения привели к появлению новой технологии RedCap и ее стандартизации в Релизах 17 и 18 Партнерского проекта 3GPP. Анализируются рыночные и технологические особенности технологии RedCap, а также особенности применение абонентских устройств RedCap в сетях 5G при оказании услуг Интернета вещей.
Выставка "Связь-2024" – главное событие ИКТ-отрасли России и СНГ
Authors: Касаткина Е.Т.
Published: March 2024

В текущем году ожидается не менее чем двукратный рост числа экспонентов самого авторитетного выставочно-конгрессного мероприятия отрасли телекоммуникаций и информационных технологий с почти полувековой историей − выставки "Связь". 36-я Международная выставка информационных и коммуникационных технологий "Связь-2024" состоится в московском Центральном выставочном комплексе "ЭКСПОЦЕНТР" с 23 по 26 апреля и станет якорным мероприятием Российской недели высоких технологий (РНВТ), объединяющей несколько выставок, форумов и конференций в сфере информационных технологий, телекоммуникаций, навигации и телематики. О главном событии телекоммуникационного года в беседе с корреспондентом "ПЕРВОЙ МИЛИ" рассказала руководитель Дирекции высокотехнологических выставок АО "ЭКСПОЦЕНТР" Е.Т.Касаткина.
Conversational AI
Authors: Kandoi Raunak , Dixit Deepali , Tyagi Mihul , Yadav Raghuraj Singh
Published: March 2024

Abstract: Conversational AI systems are becoming increasingly popular across many industries and are transforming the way people interact with technology. For a more authentic, human-like connection and a smooth user experience, these systems should combine text-based interactions with multimodal capabilities. The authors of this work suggest a new approach to improving conversational AI systems' usability by combining speech and visual analysis. By combining visual and auditory processing capabilities, AI systems can better understand human inquiries and instructions. Both visual data and speech can be better understood with the use of computer vision algorithms and natural language processing techniques, respectively. Conversational AI systems can provide more accurate and tailored replies by integrating many modalities to better grasp human intent and context. The development of multimodal conversational AI presents a significant difficulty in ensuring the smooth integration of voice and visual processing units. A strong architectural design and advanced algorithms are necessary for the simultaneous synchronization and comprehension of data from several modalities in real-time. The system needs to keep track of the conversation's context even when it switches between different forms of communication so it can keep providing fair and relevant responses all through the engagement. Customization is key to making multimodal conversational AI better for users. Based on user data and preferences, the system may tailor interactions to offer more relevant ideas and support. Users are more invested in the AI system over time, and they have a better experience overall because to customization. Ensuring the privacy and security of important audiovisual data is of the utmost importance while building multimodal conversational AI. Strong encryption, anonymization technologies, and compliance with data protection regulations are vital for user privacy and system confidence. Continuous improvement is key to the success of multimodal conversational AI systems. The feedback from users can help the developers improve the system and add new features. Thanks to this iterative technique, the AI system stays flexible and can adjust to changing consumer preferences. By combining voice and picture processing, conversational AI systems have a great deal of promise for improving the user experience. Through the integration of visual and auditory signals, these systems have the ability to comprehend user intent more accurately, provide customized experiences, and completely transform the way humans engage with technology.
Enhancing Refrigeration Efficiency through Nanoparticle-Infused Refrigerants and Phase Change Materials (PCMs) in Condenser Systems: A Comprehensive Research
Authors: Yadav Sheetal
Published: March 2024

Abstract: The evolution of refrigeration technology is at the forefront of scientific inquiry, with a focused exploration of innovative methodologies to elevate efficiency. This research paper embarks on a transformative journey within this landscape, specifically delving into the integration of nanoparticle-infused refrigerants and Phase Change Materials (PCMs) in condenser systems. The overarching objective is to unravel the synergistic effects of these groundbreaking innovations, shedding light on their profound impact on refrigeration system performance and their potential contributions to sustainability and energy efficiency. The study positions itself at the nexus of emerging technologies, aiming to provide not only a nuanced examination of nanoparticle-infused refrigerants and PCMs but also a holistic understanding of their combined influence on refrigeration processes. This multi-faceted exploration encompasses theoretical frameworks, empirical investigations, and the potential implications that these innovations carry for the broader realm of sustainable and energy-efficient refrigeration. As the global demand for energy-efficient solutions intensifies, the integration of nanoparticles into refrigerants stands as a promising avenue.
Synthesis of Silica Nanoparticles: A Review
Authors: Thacker Hency , Ram Vijay
Published: March 2024

Abstract: The prefix “nano” in the term Nanoscience or Nanoparticle accounts one billionth or 10-9 units. Nanoparticles possess unique size and shape reliant chemical, physical, magnetic and optoelectronic traits varying from that of bulk material due to its large surface area to volume ratio. There are two different approach for the synthesis of nanoparticles: Top to bottom approach and bottom to top approach. Silica nanoparticles are optimistic agents in variety of areas like drug delivery, water purification, environmental bioremediation, disease diagnostic and therapeutic applications. In the present study chemical and biogenic synthesis of silica nanoparticles with variable precursors are discussed.
Comment Toxicity Tracker Using NLP with Emphasis on Machine Learning Algorithms
Authors: Kadam Ms. Shivani , Ghatage Ms. Komal , Chaugule Mr. Aadesh , Gajarushi Mr. Shubham Dilip , Bakal Prof. J. W.
Published: March 2024

Abstract: The rise of online stages has driven to an uncommon volume of user-generated content, including comments on various forums, social media posts, and news articles. However, this abundance of user comments has also brought to light the issue of toxicity, where certain comments contain harmful, offensive, or inflammatory language that can negatively impact online discussions and communities. To address this issue, investigate centers on the advancement of a comment harmfulness location show utilizing Normal Dialect Handling (NLP) & Machine Learning. The proposed system leverages state-of-the-art NLP. By training these models on labelled datasets of toxic and non-toxic comments, the system learns to identify patterns and linguistic cues associated with toxic language. Key components of the system preprocessing steps to clean and tokenize the comments.Feature extraction using word embeddings or contextual embeddings, and model training using Machine Learning algorithms like neural networks, Random Forest Classifier model, etc . Evaluation measurements such as exactness, exactness, review, and F1-score are utilized to survey the execution of the prepared model
Detection of Autism Spectrum Disorder using Machine Learning: A Review
Authors: Sirigiri Vaishnavi , Katta Srilekha
Published: March 2024

Abstract: Social interaction and communication impairments are caused by an illness known as Autism Spectrum Disorder (ASD), which has neurological and genetic components. Statistics from the World Health Organization (WHO) show that the number of people with ASD diagnoses is progressively rising. The majority of recent research focuses on data gathering, brain image analysis, and clinical diagnosis; it does not address the diagnosis of ASD using machine learning. Currently, the only techniques available for diagnosing ASD are clinical standardized testing. This results in longer diagnostic times as well as a sharp rise in medical expenses. Machine learning approaches are being used to supplement traditional methods in order to enhance diagnosis precision and time required. The dataset is being subjected to several machine learning approaches, with the aim of constructing predictive models that are contingent on the results. The best accurate machine learning model to identify the disease in its early stages is then found by analyzing each technique according to its assessment metrics.
Review on the Impact of Waste in Nigerian Manufacturing Firms and Waste Management Practices Using Machine Learning
Authors: Chukwuebuka Isaac Chinedu
Published: March 2024

Abstract: The study examined the impact of waste in Nigeria manufacturing firm and waste management practices using machine learning. This paper presents ways of interaction of man with his environment in terms of waste management in Nigeria with the aim of promoting sustainable development. It shows the policies, the agents making these policies less likely to happen and the damage it can cause for the next generation.This article presents how data has been collected and analyzed through survey and series of progressive steps and present some guidelines when practicing waste management. It presents how a region can discover and implement some efficient technique for efficient waste management and comes up with enormous constructive results.In order to automate and optimize garbage generation, collection, transportation, treatment, and disposal, this study examines a variety of machine learning techniques. In order to provide accurate and efficient forecasts for trash creation, segregation, and collection, the system combines several machine learning techniques, such as decision trees (DT), random forests (RF), k-nearest neighbors (KNN), support vector machines (SVM), and clustering algorithms