bluuebunny/crossref_metadata_2025_split huggingface dataset:
Whole Genome Targeted Enrichment and Sequencing of Human-Infecting Cryptosporidium spp.
Authors: Bayona-Vásquez Natalia , Sullivan Amanda , Beaudry Megan , Khan Asis , Baptista Rodrigo , Petersen Kelly , Bhuiyan Mohammad , Brunelle Brian , Robinson Guy , Chalmers Rachel , Alves-Ferreira Eliza , Grigg Michael , Kissinger Jessica , Glenn Travis 
Published: May 2024

Abstract

Cryptosporidium spp. are protozoan parasites that cause severe illness in vulnerable human populations. Obtaining pure Cryptosporidium DNA from clinical and environmental samples is challenging because the oocysts shed in contaminated feces are limited in quantity, difficult to purify efficiently, may derive from multiple species, and yield limited DNA (<40 fg/oocyst). Here, we develop and validate a set of 100,000 RNA baits (CryptoCap_100k) based on six human-infecting Cryptosporidium spp. (C. cuniculus, C. hominis, C. meleagridis, C. parvum, C. tyzzeri, and C. viatorum) to enrich Cryptosporidium spp. DNA from a wide array of samples. We demonstrate that CryptoCap_100k increases the percentage of reads mapping to target Cryptosporidium references in a wide variety of scenarios, increasing the depth and breadth of genome coverage, facilitating increased accuracy of detecting and analyzing species within a given sample, while simultaneously decreasing costs, thereby opening new opportunities to understand the complex biology of these important pathogens.
A Biomarker-Based Framework for the Prediction of Future Chronic Pain
Authors: Vachon-Presseau Etienne , Fillingim Matt , Tanguay-Sabourin Christophe , Parisien Marc , Zare Azin , Guglietti Gianluca , Norman Jax , Petre Bogdan , Bortsov Andre , Ware Mark , Perez Jordi , Roy Mathieu , Diatchenko Luda 
Published: May 2024

Abstract

Chronic pain is a multifactorial condition presenting significant diagnostic and prognostic challenges. Biomarkers for the classification and the prediction of chronic pain are therefore critically needed. In this multi-dataset study of over 523,000 participants, we applied machine learning to multi-dimensional biological data from the UK Biobank to identify biomarkers for 35 medical conditions associated with pain (e.g., clinical diagnosis of rheumatoid arthritis, fibromyalgia, stroke, gout, etc.) or self-reported chronic pain (e.g., back pain, knee pain, etc). Biomarkers derived from blood immunoassays, brain and bone imaging, and genetics were effective in predicting medical conditions associated with chronic pain (area under the curve (AUC) 0.62–0.87) but not self-reported pain (AUC 0.50–0.62). Among the biomarkers identified was a composite blood-based signature that predicted the onset of various medical conditions approximately nine years in advance (AUC 0.59–0.72). Notably, all biomarkers worked in synergy with psychosocial factors, accurately predicting both medical conditions (AUC 0.69–0.91) and self-report pain (AUC 0.71–0.92). Over a period of 15 years, individuals scoring high on both biomarkers and psychosocial risk factors had twice the cumulative incidence of diagnoses for pain-associated medical conditions (Hazard Ratio (HR): 2.26) compared to individuals scoring high on biomarkers but low on psychosocial risk factors (HR: 1.06). In summary, we identified various biomarkers for chronic pain conditions and showed that their predictive efficacy heavily depended on psychological and social influences. These findings underscore the necessity of adopting a holistic approach in the development of biomarkers to enhance their clinical utility.
Clinical predictive model for identifying high-risk factors in Pleurisy Tuberculoma patients
Authors: Gao weiwei , Yang Chen , Wang Tianzhen , Guo Yicheng , Dai Guangchuan , Hu Weiyi , Chen Shanshan , Tang Xiaoli , Yin Chunyang , Chen Cheng , Zeng Yi 
Published: May 2024

Abstract

Introduction
: Pleurisy tuberculoma(PTM) manifests as a tumor-like lesion in either the parietal or visceral pleura. The majority of PTM cases arise during the management of tuberculous pleural effusion, underscoring a strong association between the two conditions, despite the precise pathogenesis remaining elusive. To explore the high-risk factors for the development of PTM, we constructed a clinical predictive model aimed at providing more powerful clues for PTM treatment.
Methods
A retrospective study was conducted on patients with PTM or TPE who were treated at Nanjing Chest Hospital and Nanjing Second Hospital between March 2013 and April 2024. The demographic information and clinical characteristics were sorted out, and the model was constructed by logistic regression, lasso regression, and best subset selection. ROC curves, calibration curves, DCA curves, and clinical impact curves were analyzed for all constructed models to determine the final clinical prediction model. In the internal validation, the performance of the selected model was further evaluated.
Results
The final predictive model incorporated two risk factors associated with PTM development: Ki-67+CD4+T and Ki-67+CD8+T. Despite its limited calibration, the model has a strong discriminating ability (c-statistic = 0.987; 95% confidence interval: 0.969-1.000) and holds significant clinical value (as indicated by DCA analysis, maintaining a high net benefit across a wide threshold range). During internal validation, the model exhibited excellent predictive performance (AUC: 0.98; Accuracy: 0.96; Kappa: 0.93; Sensitivity: 0.95; Specificity: 0.96).
Conclusion
A predictive model based on two indicators may help clinicians make early predictions of PTM based on laboratory data. The results of this study may motivate healthcare providers to further investigate the immune role of T lymphocytes in PTM individuals, enhancing comprehension of the development of PTM.
From Diagnosis to Management: Unveiling the Challenges of Artificial Intelligence Solutions in Cardiovascular Healthcare
Authors: Idakwo Valentine 
Published: May 2024

Abstract

Cardiovascular diseases (CVDs) are the leading cause of mortality in the world. Artificial Intelligence (AI) offers an opportunity to improve the quality of care provided to cardiovascular patients due to its ability to handle large and complex data. Despite promising results obtained in several studies, widespread adoption of AI in cardiovascular care is lacking due to the existence of some gaps. The goal of this study is to analyze the existing challenges faced by AI solutions in cardiovascular care. This study adopted a mixed-methods research approach, combining semi-structured interviews with responses from a self-administered online survey. A total of 5 interviews were conducted and 91 valid survey responses were obtained. Survey respondents included doctors, nurses, medical researchers, health I specialists, hospital administrators, and other clinically affiliated participants working with cardiovascular patients. Participants identified 8 major challenges: data-related challenges, regulatory challenges, infrastructural challenges, gaps in knowledge, transparency challenges, ethical challenges, issues with change management, and acceptance challenges. These gaps hinder the adoption of AI in cardiovascular care and taking proactive measures to address these challenges is key to fostering AI adoption.
Enhancing Patient Rehabilitation Outcomes: Artificial Intelligence-Driven Predictive Modeling for Home Discharge in Neurological and Orthopedic Conditions
Authors: Buscarini Leonardo , Romano Paola , Cocco Elena Sofia , Damiani Carlo , Pournajaf Sanaz , Franceschini Marco , Infarinato Francesco 
Published: May 2024

Abstract

In recent years, the fusion of the medical and computer science domains has gained significant traction in the scientific research landscape. Progress in both fields has enabled the generation of a vast amount of data used for making predictions and identifying interesting clusters and pathways. The Machine Learning model's application in the medical domain is one of the most compelling and challenging topics to explore, bridging the gap between Artificial Intelligence (AI). and healthcare. The combination of AI and medical information offers the possibility to create tools that can benefit both healthcare providers and physicians. This enables the enhancement of rehabilitation therapy and patient care. In the rehabilitation context, this work provides an alternative perspective: prediction of patients’ home discharge upon completing the rehabilitation protocol. Demographic and clinical data were collected on 7282 inpatients from electronic Medical Record, each record was categorized into Neurological Patients (NP, N = 3222) or Orthopedic Patients (OP, N = 4060). To comprehend the most suitable machine learning model, an extensive data preprocessing phase was essential. This included steps such as variable recoding, scaling, and comparing various dataset balancing methods to enhance the model’s performance. Random Forest model was selected after a careful review and comparison of algorithms commonly utilized in the clinical-rehabilitative domain. Following a significant hyperparameter optimization phase through a grid search technique, we achieved an accuracy rate of 97% for OP and slightly lower (94%) for NP. This work points out the increasing importance of AI in medicine, especially in the realm of personalized rehabilitation. The use of such approaches could signify a transformative shift in healthcare. The integration of machine learning not only enhances the precision of treatment but also opens new possibilities for patient-centered care, improving outcomes and quality of care for individuals undergoing rehabilitation.
Cardiac Dysfunction in Trisomy 18 with Ventricular Septal Defect after Pulmonary Artery Banding
Authors: Kishi Kanta , Nemoto Shintaro , Ozaki Noriyasu , Odanaka Yutaka , Ashida Atsuko , Konishi Hayato , Suzuki Akiyo , Ashida Akira 
Published: May 2024

Abstract

Although palliative pulmonary artery banding (PAB) is widely known to contribute to hospital discharge in infants with trisomy 18 (T18) and ventricular septal defect (VSD), the long-term prognosis remains unsatisfactory unless corrective surgery is performed. This study aimed to explore the possible reasons for poor outcomes of PAB in patients with T18 and VSD using hemodynamic and morphological evaluations. Echocardiography was used to assess the ventricular function via tissue Doppler imaging (TDI). Based on the values for healthy individuals, comparisons were made between infants with T18 and those with normal karyotype (NK) who underwent PAB for VSD. Right and left ventricular functions were impaired after PAB in infants with T18 and NK with VSD compared to those in healthy infants. The parameters indicating left ventricular function were similar after PAB between the infants with T18 and NK with VSD; however, the PAB-impaired right ventricular (RV) diastolic function was significantly worse in the infants in the T18 subgroup than those in the NK subgroup. Interestingly, RV diastolic dysfunction was significantly and positively correlated with elevated RV end-diastolic pressure and serum N-terminal pro-brain natriuretic peptide levels. Myocardial hypertrophy and interstitial fibrosis were observed in the resected RV muscle of patients with T18. RV diastolic dysfunction progressed after PAB in patients with T18 and a large VSD, possibly because of excessive RV hypertrophy. These findings may contribute to poor outcomes.
Prompting and In-Context Learning: Optimizing Prompts for Mistral Large
Authors: Higginbotham Grant Z. , Matthews Nathan S. 
Published: May 2024

Abstract

The exploration of the synergy between prompting and in-context learning reveals significant improvements in the performance of language models when tailored instructions and relevant context are integrated. The research delves into various prompt designs, assessing their impact on tasks such as text summarisation, machine translation, and question-answering. Prompts that include clear, explicit instructions and contextual information significantly enhance model outputs in terms of accuracy, coherence, and relevance. Experiments with the Mistral Large model demonstrate that adaptive prompting, which dynamically adjusts based on real-time interactions, can further refine model performance. Challenges such as balancing the amount of context to avoid information overload and the sensitivity of model responses to subtle changes in prompt phrasing are addressed. The study's findings underscore the critical role of effective prompt engineering and contextual integration in maximising the potential of language models. Future research directions include developing systematic methods for prompt design, optimising contextual information, and exploring cross-task generalisation. This research contributes valuable insights into guiding and informing language models, paving the way for more intelligent and adaptive AI systems across diverse applications.
Clinical Presentation, Diagnosis, and Management of Hyperinsulinemic
Hypoglycemia in Adults: A Single-center Experience
Authors: Cordenos Giulia , Piccinno Elia , Cominacini Mattia , Davì Maria Vittoria 
Published: May 2024

Introduction:
Hyperinsulinemic Hypoglycemia (HH) is a rare condition characterized
by inappropriately elevated insulin levels in the presence of low glucose levels. A proper
diagnostic framework is fundamental to avoid patients undergoing unnecessary diagnostic and
therapeutic invasive procedures.

Objective:
The study aimed to assess the clinical presentation, diagnostic work-up, and treatment
of a single-center cohort of patients affected by HH.

Methods:
We conducted a retrospective analysis of data collected from January 2000-2023

Results:
Our study included 104 patients: 81 (58% F) affected by insulinoma, 11 (91% F) by
autoimmune hypoglycemia, 7 (71% M) by post-gastric surgery hypoglycemia, and 5 (80% F) by
factitious hypoglycemia. HH was more frequent in females (63 F vs. 41 M, p-value 0.039). The
median age at diagnosis was lower in insulinoma than in the autoimmune group (52.7 vs. 63.7 y,
p < 0.001). During the hypoglycemic event, insulin and C-peptide levels were significantly
higher in autoimmune hypoglycemia than in insulinoma (insulin 324.6 vs. 36.4 μU/ml, p-value
0.033; C-peptide 14.25 vs. 3.99 ng/ml, p-value 0.003). Specifically, C-peptide levels < 9.6 ng/ml
and insulin levels < 75 μU/ml exhibited 97.3% vs. 93.4% sensitivity and 80% vs. 90% specificity
for insulinoma diagnosis, respectively. Regarding insulinoma, the sensitivity of localizing imaging
was 88% for Endoscopic Ultrasound (EUS), 86% for Magnetic Resonance Imaging (MRI),
82% for Computed Tomography (CT) scan, 52% for nuclear imaging, and 100% for angiography
with the Doppman test. Among insulinoma patients, 79% received surgical treatment
while 4% radiofrequency ablation. Symptomatic remission occurred in 100% of cases.

Conclusion:
We have confirmed insulinoma as the primary cause of HH. The autoimmune form
should be suspected when insulin and C-peptide levels are markedly elevated.
Healing Effect of Hydroalcoholic Extracts of Thymbra Spicata and
Ferulago Angulata on Second-degree Burn Wounds in Rats
Authors: Ghaneialvar Huri , Alizadeh Reza , Akbari Amir Reza , Yari Milad , Mansouri Kazhal , Ahmadi Alireza , Mami Sajad , Haddadi Mohammad Hossein , Bahmani Mahmoud 
Published: May 2024

Objective:
Burns can be physically and mentally painful, making them one of the
worst experiences. In this study, hydroalcoholic extracts of Thymbra spicata and Ferulago angulata
were investigated as treatments for second-degree burns in male Wistar rats.

Methods:
To create the burn wounds, a circular metal tool with a diameter of 2 cm was used,
followed by treatment with prepared ointments including Ferulago angulata (1%), Thymbra spicata
(1%), Thymbra spicata plus Ferulago angulata (1%), and Adib derm ointment. The wound
area was measured and the wound tissue was examined histopathologically on days 1, 7, 14, and
21 in each group. The ointments were applied every day for 21 days to determine the wound
healing rate.

Results:
The experiment showed that on the sixth and eleventh days, the groups treated with a
combination of Ferulago angulata and Thymbra spicata, and Ferulago angulata alone, demonstrated
significant reductions in wound area. According to histopathology, both Ferulago angulata
alone and in combination with Thymbra spicata significantly improved the inflammatory
condition, epithelialization process, fibroplasia, arrangement, and density of collagen fibers
when compared to the other groups.

Conclusion:
The study showed that Ferulago angulata extract, either on its own or in combination
with Thymbra spicata extract, has significant healing and restorative effects on seconddegree
burn wounds in animal models.
Evaluating the Current State of Pedestrian Facilities in Peri-Urban and Urban Areas: A Case Study of Pietermaritzburg City
Authors: Maseko Wealthy Tsembile , Adedeji Jacob Adedayo , Bashingi Ndakhona , Honiball James 
Published: May 2024

Background
Pedestrian infrastructure is pivotal for advancing the movement of nonmotorized road users and plays a transformative role in transitioning toward sustainable transportation, especially in rural territories influenced by urban dynamics and interconnected by daily commuter flows. Both walking and cycling stand out as paramount sustainable transport means, offering substantial health and environmental benefits, including reduced noise and air pollution. Multiple factors, such as convenience, safety, accessibility, service cost, flexibility, and the quality of pedestrian pathways, shape individuals' propensity to walk. Notably, in peri-urban contexts, the cost of service becomes a significant determinant, as a notable proportion of the population earns minimal wages or faces unemployment.

Objective
This study sought to examine the current state of pedestrian walkways within Pietermaritzburg and surrounding areas, through which the accessibility, availability, and quality of these facilities were assessed. The study has suggested possible solutions to address the challenges faced by pedestrians.

Methods
By conducting semi-structured interviews and leveraging ArcGIS mapping utilities, this research has delineated the existing scenario and offered an initial redesign tailored to pedestrian requirements.

Results
The data have starkly revealed pervasive inadequacies in the existing pedestrian infrastructure. The functionality and safety of current amenities have been manifestly undermined by these shortcomings. The continuing use of these dilapidated facilities may pose alarming threats not only to pedestrians, but also to other road users.
Application of high-power ablation mode in patients with idiopathic ventricular extrasystole
Authors: Prorok S.Yu. 
Published: May 2024

Background. A new method of high-power short-duration ablation (HPSDA) with high impact energy, ranging from 60 to 90 W, is becoming widespread in the world. However, the number of studies on the use of HPSDA in patients with idiopathic ventricular arrhythmias (IVA) is limited. It is relevant to study the feasibility of using HPSDA to improve the outcomes of radiofrequency ablation (RFA) in patients with IVA. The aim of the study: to evaluate the immediate results of HPSDA in RFA for IVA and to compare it with the classic ablation mode. Materials and methods. We have retrospectively analyzed the results of RFA in 54 patients with symptomatic IVA. Their average age was 45.0 ± 11.7 years. The majority were men (72.3 %). Given the possible effect of intracardiac mapping on the results of ablation, the patients were divided into two groups. Group I include 27 (50 %) participants in whom mapping was performed using 3D navigation. Group II include 27 (50 %) patients in whom 3D navigation was not used. The decision to use HPSDA or classic ablation was made empirically, without considering any factors or characteristics of a patient. The following ablation parameters were used during HPSDA: temperature 45 °C, exposure time 7 seconds, energy 70 W. Results. In group I, where 3D navigation was used to localize the substrate, HPSDA was performed in 12 patients (44.4 %), and the classic mode was used in 15 cases (55.6 %). With conventional mapping, HPSDA was applied in 10 (37.0 %) patients, and the classic mode in 17 (63.0 %). Сlassic ablation allowed to achieve complete arrhythmia suppression in all patients, regardless of the mapping method. When using only HPSDA, the disappearance of arrhythmia was observed only in 45.5 % of cases. With the use of the classic mode, recurrence of IVA during the control time was observed in 16.6 % of patients, while with the use HPSDA in 33.3 %. Conclusions. The high-power regimen demonstrated significantly lower efficacy for permanent suppression of IVA compared to classic ablation methods (p = 0.007). The use of HPSDA to suppress IVA is associated with a high risk of arrhythmia recurrence during control time (p = 0.0010). It is advisable to convert the ablation mode for complete suppression of arrhythmia when the HPSDA is ineffective.
Chlamydia abortus in brown pelicans (Pelecanus occidentalis) from the gulf of California, Mexico
Authors: Limón-González Magalena , Avalos-Téllez Rosalía , Palomares Resendiz Erika Gabriela , Hernández-Castro Rigoberto , Díaz Aparicio Efren 
Published: May 2024

The brown pelican (Pelecanus occidentalis californicus) is a migratory subspecies that nests on the islands of the gulf of California, in the Pacific Northwest. The objective of this study was to identify Chlamydia species in brown pelican samples obtained in 2016, 2017, and 2021. Samples were taken from the cloacae and choanae of 86 brown pelicans in specimens ranging from three to seven weeks of age. The samples were obtained with sterile swabs and transferred to an SPG growth medium. The DNA was extracted to carry out a real-time PCR that detects the gene of the 235 rRNA subunit of the Chlamydiaceae family. In addition, real-time PCR directed to the ompA gene was performed to determine the Chlamydia species. Of the 86 sampled pelicans, four were positive for the family Chlamydiaceae and Chlamydia abortus. This is the first report of C. abortus in brown pelicans from the gulf of California, Mexico.