bluuebunny/crossref_metadata_2025_split huggingface dataset:
Antenatal Detection of Paediatric Surgical Congenital Abnormalities and its Impact on Maternal Anxiety: A Multicentre Prospective Study in a Middle-Income Country
Authors: Nah Shireen Anne , Singaravel Srihari , Tan Yew Wei , Sanmugam Anand , Tan Wei Sheng , Soh Chai Hoon , Woon Seet Fai , Chandran Bathmavathy 
Published: November 2024

Abstract

Background: In middle-income countries, healthcare systems face unique challenges in ensuring timely antenatal detection of congenital abnormalities that require paediatric surgical intervention. Early detection can significantly improve outcomes, yet resource constraints often limit access to diagnostic technologies. This study evaluates the antenatal detection rate of congenital abnormalities referred to paediatric surgical services in three Malaysian tertiary centres and examines its effect on maternal anxiety.
Methods: A prospective observational study was conducted at Hospital Raja Permaisuri Bainun (HRPB), Hospital Sultanah Aminah (HSA), and University of Malaya Medical Centre (UMMC) over a 8 month period. Data were collected from mothers of neonates born with congenital abnormalities, and the Spielberg State-Trait Anxiety Inventory (STAI) was used to assess maternal anxiety.
Results: A total of 58 patients were recruited. The overall antenatal detection rate for congenital abnormalities was 43.1%, comparable to high-income countries despite resource limitations. Antenatal counselling by paediatric surgeons was rare (6.9%) and showed no significant reduction in maternal anxiety (p = 0.374).
Conclusion: The antenatal detection rates at the three Malaysian centres align with those in more developed nations, underscoring the potential of middle-income healthcare systems to deliver high-quality prenatal care. However, improving access to diagnostic technologies and involving paediatric surgical teams in antenatal counselling could further enhance care.
Polysorbate-80 coated Galantamine-Chitosan nanoparticles for enhanced safety and efficacy: Pharmacokinetics and Brain distribution in Rats
Authors: Natarajan Tamilselvan , Chellan Vijaya Raghavan , Rahman Habibur , Siram Karthick , Karuppaiah Arjunan , Gopinathapillai Prasanth Kalavalarasariel 
Published: November 2024

Abstract

In brain drug delivery, the BBB (Blood-Brain Barrier) has been a great barrier for various active pharmaceutical drugs. Hence, developing suitable drug delivery systems are need of the hour. However, the present study aimed to develop a polysorbate-80 (PS-80) coated galantamine (GAL) loaded chitosan nanoparticles (CS-NPs) with particles less than 200 nm to enhance the drug internalization in the brain. GAL-CS-NPs were prepared using the ionic gelation technique and optimized to obtain a particle size of less than 200nm and further coated wirh PS-80 to obtain polysorbate-80 coated galantamine loaded chitosan nanoparticles (PS-80-GAL-CS-NPs) The physiochemical properties of uncoated GAL-CS-NPs, PS-80 coated GAL-CS-NPs and the in-vitro evaluations, such as cytotoxicity and cellular internalization in SH-SY-5Y human neuroblastoma cell lines, were studied. The particle size of optimized PS-80-GAL-CS-NPs was observed to be 115±4 nm, and zeta potential was found to be 31.2±1.7 mV. While the drug entrapment efficiency was found to be 65.5±1.2 %. The in-vitro drug release of PS-80-GAL-CS-NPs was found to be 56.75±1.3 %. However, the cytotoxicity studies did not show any toxicity for the drug concentrations of 10 and 100 µg/mL. PS-80-GAL-CS-NPs facilitated time-dependent GAL uptake in SH-SY-5Y cell lines. Studies conducted in vivo in plasma revealed a steady release of GAL from PS-80-GAL-CS-NPs. The distribution PS-80- GAL-CS-NPs in the brain after oral administration at 1st, 4th and 8th hr was found to be 1.7, 3.1 and 2.0 folds higher when compared to the uncoated NPs. Further, the histopathological study did not show any morphological changes in the rat brain. This study indicates that PS-80-GAL-CS-NPs might be a promising delivery method for Alzheimer’s Disease (AD)
Exploring the Correlation Between Gallstone Disease and Sarcopenia Risk: Insights from a Cross-Sectional Analysis
Authors: Zeng Di , Lu Jiong , Chen Jinhong 
Published: November 2024

Abstract

Background Musculoskeletal disorders related to chronic liver and biliary diseases are increasingly recognized, with a growing body of observational studies linking these conditions. This study investigates the association between gallstone disease and sarcopenia, exploring the implications for patient health outcomes.Methods A cross-sectional analysis was conducted involving 1,683 participants to assess the relationship between gallstones and sarcopenia. Adjusted odds ratios (OR) for sarcopenia were calculated using logistic regression models, controlling for sociodemographic and health-related factors.Results Among the participants, those with gallstones had a higher likelihood of sarcopenia, with adjusted odds ratios of 2.15 in Model 1 and 2.71 in Model 2. The risk was particularly pronounced in females, with an odds ratio of 2.756. Additionally, individuals with diabetes faced an elevated odds ratio of 8.576 for developing sarcopenia. Although trends in increased risk were observed in other subgroups, such as those with hypertension and younger adults, these did not achieve statistical significance. An inverse relationship was also noted between appendicular skeletal muscle mass (ASM) and gallstone occurrence.Conclusion This study demonstrates that gallstone disease increases the risk of sarcopenia, highlighting the need for integrated management strategies that include nutritional evaluations and exercise interventions. Further longitudinal research is essential to clarify causal relationships and underlying mechanisms.
Longitudinal qualitative assessment of meaningful symptoms and relevance of WATCH-PD digital measures for people with early Parkinson’s
Authors: Mammen Jennifer R. , Lerner Aaron , Al-Rubayie Raunak , Kostrzebski Melissa , Stephenson Diane , Xiao Yuge , Redmond Chris , Tyo Mirinda , Reddy Varun G. , Auinger Peggy , Dorsey E. Ray , Adams Jamie L. 
Published: November 2024

Abstract

Background: Longitudinal qualitative data on what matters to people with Parkinson's disease are lacking and needed to guide patient-centered clinical care and development of outcome measures.
Objective: To evaluate change over time in symptoms, impacts, and relevance of digital measures to monitor disease progression in early Parkinson’s.
Methods: In-depth, online symptom mapping interviews were conducted with 33 people with early Parkinson's at baseline and one year later to evaluate (A) symptoms, (B) impacts, and (C) relevance of digital measures to monitor personally relevant symptoms. Maps and transcripts were coded for frequencies, Likert scale rankings (0=Not present to 4=Most bothersome), and thematic findings. Wilcoxon Signed Rank test was used to evaluate change over time.
Results: Other than walking and balance, most motor symptoms did not change significantly from baseline to one year later. Multiple significant changes were observed in non-motor areas (cognition, speech, sleep, mood, fatigue, pain; p<0.05) and functional impacts (mobility, effort to do usual activities, personal comfort; p<0.05). Thematic analysis revealed ability to cope with and compensate for actual or anticipatedsymptoms reduced disruptions to well-being and changed how bothersome symptoms were. All digital measures targeted symptoms that were personally important to most participants (>80%).
Conclusion: Non-motor and walking/balance symptoms changed sooner than other motor symptoms during the course of one year. Evaluation of coping and compensatory mechanisms may be essential to understanding symptom bothersomeness at a given point in time. Smartphone and smartwatch digital measures were relevant to personally meaningful symptoms of early PD.
Employing the agricultural water footprint concept to enhance the sustainable management of water resources: a review
Authors: Mizyed Amjad , Moghier Yunes , Hamada Mazen 
Published: November 2024

ABSTRACT

Agriculture is recognized as a significant consumer of water, with projections indicating an increase in consumption due to rising food demand and the impacts of climate change. Therefore, every country must ensure the adequacy and security of water for sustainable uses. This research reviews the water footprint (WF) index as a recent water indicator that measures the human appropriation of water resources for several consumptive uses that qualify decision-makers to manage water efficiently in agriculture sectors. Light is shed on the WF concept, virtual water, mechanisms of water use, and the applications in water resources management, as well as methods for assessment with its three components, blue, green, and gray. The comparison between these methods was discussed, and the differences and motives for use were clarified. The good innovation in this review is linking sustainability considerations with the WF, discussing measuring the sustainability of agricultural activities, and deducing the best policies and decisions to meet environmental and economic needs without compromising the future requirements. This outcome confirms the effectiveness of applying the WF in enhancing sustainability.
Mass transfer process in the adsorption of Cibacron Blue (CB) on untreated pea pods (UPPs): kinetic and thermodynamic studies
Authors: Abbas Moussa , Trari Mohamed 
Published: November 2024

ABSTRACT

In recent decades, there has been extensive use of synthetic dyes in the dye-based industries, particularly the textile sector. Therefore, the aim of this study was to evaluate the adsorption of Cibacron Blue (CB) onto untreated pea pods (UPPs) in a batch system. The effects of the initial CB concentration (10–20 mg/L), solution pH (2–12), adsorbent dose (0–4 g/L), particle size (50–500 μm), and temperature (295–318 K) on the CB adsorption were investigated in batch configuration to determine the optimum conditions. Analyses of UPPs were characterized by Fourier Transform Infrared (FTIR) spectroscopy, Brunauer–Emmett–Teller (BET), and point-of-zero charge (pHpzc = 5.6). Under optimized conditions (pH: 2.5, particle size: 50 μm, time: 40 min, adsorbent dose: 2.5 g/L, and agitation speed: 250 rpm), up to 30.30 mg/g at 25 °C is removed from the solution. The adsorptions kinetics obey rather a pseudo-second-order kinetic model with a determination coefficient of R2 = 0.999. The adsorption isotherms have been used for the determination of thermodynamic parameters, i.e. the negative free energy ΔG° (−4.33 to 0.783 kJ/mol), negative enthalpy change ΔH° (−54.63 kJ/mol), and entropy (ΔS° = −0.1705 kJ/mol.K) indicate that the CB adsorption onto UPPs is spontaneous and exothermic in nature.
A universal empirical equation to estimate the abundance of carbapenem-resistant genes during aerobic digestion of wastewater sludge
Authors: Poorasgari Eskandar , Örmeci Banu 
Published: November 2024

ABSTRACT

Carbapenem-resistant genes (CRGs) exist in wastewater and accumulate in wastewater sludge. Due to the potential threat posed by the CRGs, it is important to quantify CRGs and predict their removal and discharge concentrations during aerobic sludge digestion. Nonetheless, gene quantification is tedious, error-prone and expensive. This study aims to develop multiple regression models to estimate CRGs from sludge parameters that are routinely measured for the monitoring and design of aerobic sludge digesters. Batch reactors were operated at mesophilic and thermophilic temperatures for 20-35 days. Sludge samples were periodically taken during aerobic digestion. Three CRGs (blaGES, blaOXA-48 and blaIMP-27) together with 16S rRNA and integron class 1 genes were quantified. Aerobic digestion reduced the abundance of all target genes. Multiple regression modelling was conducted in linear (LM) and non-linear (NLM) modes. Sums of squared errors of the LM models were 0-0.048, whereas those of the NLM models were 0–0.003. Adjusted R2 ranges of the LM and NLM models were 0.774–0.931 and 0.986–1, respectively. Overall, the NLM models predicted the abundance of target genes more accurately than the LM models. NLM models may be used to modify the design and operational parameters of aerobic sludge digesters.
Network Pharmacological Analysis of Hydroxychloroquine Intervention in the Treatment of Iga Nephropathy
Authors: Zou Mengxiao , Xu Gang , Ge Shuwang , Guo Kanglin , Duo Qian , Cheng Yichun 
Published: November 2024

Background:
IgA nephropathy (IgAN) is the most prevalent primary glomerulonephritis globally
and has a high propensity to develop into end-stage renal disease (ESRD). Hydroxychloroquine has been
proven to reduce proteinuria in IgAN patients, but the precise mechanism remains unclear. Therefore, network
pharmacology was used to investigate the mechanism.

Methods:
PubChem and SwissADME databases were utilized to acquire the structure of hydroxychloroquine.
The SwissTargetPrediction, PharmMapper, DrugBank, TargetNet, and BATMAN-TCM databases were then
utilized to obtain the targets. The target genes related to IgAN were then gathered from the databases, which
included GeneCards, PHARMGKB, DrugBank, OMIM, and DisGeNET. Common targets were obtained by
UniProt. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses
were performed to define the main molecular mechanisms and pathways. Furthermore, a protein-protein interaction
(PPI) network was constructed using the STRING tool, and the core targets were obtained by Cytoscape.
Finally, molecular docking between the core targets and hydroxychloroquine was performed.

Results:
167 common target genes were acquired by overlapping. The core targets were TNF, ALB, IL1B,
JUN, FOS, SRC, and MMP9. The GO and KEGG results showed the targets to be related to the production of
inflammatory cytokines and chemokines and were engaged in the toll-like receptor (TLR) signaling pathway.
At the same time, the molecular docking results showed that the core targets all combined with hydroxychloroquine
closely.

Conclusion:
This study proved that hydroxychloroquine may treat IgAN through the TLR signaling pathway,
and the restraint of TNF, TLR, IL1B, and JUN may be essential for the treatment.
Effects of Gadolinium Chelate Administration Timing on T2-weighted and Diffusion-weighted Abdominal MRI Examination: A Prospective Study
Authors: Jia Su-Lan , Xu Hui , Yang Da-Wei , Ren A-Hong , Yang Zheng-Han 
Published: November 2024

Background:
Magnetic Resonance Imaging (MRI) data acquisition includes several sequences that might be optimized to reduce the scan time.

Objective:
This study aimed to investigate the impact of gadolinium chelate administration timing on scan duration and image quality in Diffusion-weighted
Imaging (DWI) and T2-weighted Imaging (T2WI) during abdominal MRI examinations.

Methods:
A prospective study was conducted from October 2018 to May 2020. Study participants were assigned into a conventional group, undergoing MRI
with DWI and T2WI sequences pre and post-gadolinium injection, or an optimized group, receiving MRI with DWI and T2WI sequences after
gadolinium injection. Quantitative image quality, measured by the Signal-to-noise Ratio (SNR), Contrast-to-noise Ratio (CNR), and Apparent
Diffusion Coefficient (ADC), was analyzed. Kappa statistics were employed for the inter-observer agreement on liver lesion detection.

Results:
Our study has included 341 patients, with 168 and 173 in the conventional and optimized groups, respectively. Mean scan durations were 1,304
(±143) and 1,015 (±129) s for the conventional and optimized groups, respectively (p<0.05). For the liver, spleen, and pancreas, SNR and ADC
remained statistically unchanged in post-enhanced DWI and T2WI (p>0.05). Significant decreases in the SNR and ADC of the kidney were
observed in post-contrast DWI and T2WI (p<0.05). Hepatic lesion detectability did not show significant differences between pre and post-contrast
DWI and T2WI images (p>0.05).

Conclusion:
DWI and T2WI sequences assessed post-gadolinium administration exhibited shortened scan time without compromising the image quality for
liver, spleen, and pancreas evaluations. However, these sequences should be examined before gadolinium administration when assessing the
kidneys.
The Potential of Artificial Intelligence Tools for Reducing Uncertainty in Medicine and Directions for Medical Education
Authors: Alli Sauliha Rabia , Hossain Soaad Qahhār , Das Sunit , Upshur Ross 
Published: November 2024

Abstract
In the field of medicine, uncertainty is inherent. Physicians are asked to make decisions on a daily basis without complete certainty, whether it is in understanding the patient’s problem, performing the physical examination, interpreting the findings of diagnostic tests, or proposing a management plan. The reasons for this uncertainty are widespread, including the lack of knowledge about the patient, individual physician limitations, and the limited predictive power of objective diagnostic tools. This uncertainty poses significant problems in providing competent patient care. Research efforts and teaching are attempts to reduce uncertainty that have now become inherent to medicine. Despite this, uncertainty is rampant. Artificial intelligence (AI) tools, which are being rapidly developed and integrated into practice, may change the way we navigate uncertainty. In their strongest forms, AI tools may have the ability to improve data collection on diseases, patient beliefs, values, and preferences, thereby allowing more time for physician-patient communication. By using methods not previously considered, these tools hold the potential to reduce the uncertainty in medicine, such as those arising due to the lack of clinical information and provider skill and bias. Despite this possibility, there has been considerable resistance to the implementation of AI tools in medical practice. In this viewpoint article, we discuss the impact of AI on medical uncertainty and discuss practical approaches to teaching the use of AI tools in medical schools and residency training programs, including AI ethics, real-world skills, and technological aptitude.
An Intelligent System for Classifying Patient Complaints Using Machine Learning and Natural Language Processing: Development and Validation Study
Authors: Li Xiadong , Shu Qiang , Kong Canhong , Wang Jinhu , Li Gang , Fang Xin , Lou Xiaomin , Yu Gang 
Published: November 2024

Background
Accurate classification of patient complaints is crucial for enhancing patient satisfaction management in health care settings. Traditional manual methods for categorizing complaints often lack efficiency and precision. Thus, there is a growing demand for advanced and automated approaches to streamline the classification process.

Objective
This study aimed to develop and validate an intelligent system for automatically classifying patient complaints using machine learning (ML) and natural language processing (NLP) techniques.

Methods
An ML-based NLP technology was proposed to extract frequently occurring dissatisfactory words related to departments, staff, and key treatment procedures. A dataset containing 1465 complaint records from 2019 to 2023 was used for training and validation, with an additional 376 complaints from Hangzhou Cancer Hospital serving as an external test set. Complaints were categorized into 4 types—communication problems, diagnosis and treatment issues, management problems, and sense of responsibility concerns. The imbalanced data were balanced using the Synthetic Minority Oversampling Technique (SMOTE) algorithm to ensure equal representation across all categories. A total of 3 ML algorithms (Multifactor Logistic Regression, Multinomial Naive Bayes, and Support Vector Machines [SVM]) were used for model training and validation. The best-performing model was tested using a 5-fold cross-validation on external data.

Results
The original dataset consisted of 719, 376, 260, and 86 records for communication problems, diagnosis and treatment issues, management problems, and sense of responsibility concerns, respectively. The Multifactor Logistic Regression and SVM models achieved weighted average accuracies of 0.89 and 0.93 in the training set, and 0.83 and 0.87 in the internal test set, respectively. Ngram-level term frequency–inverse document frequency did not significantly improve classification performance, with only a marginal 1% increase in precision, recall, and F1-score when implementing Ngram-level term frequency–inverse document frequency (n=2) from 0.91 to 0.92. The SVM algorithm performed best in prediction, achieving an average accuracy of 0.91 on the external test set with a 95% CI of 0.87-0.97.

Conclusions
The NLP-driven SVM algorithm demonstrates effective classification performance in automatically categorizing patient complaint texts. It showed superior performance in both internal and external test sets for communication and management problems. However, caution is advised when using it for classifying sense of responsibility complaints. This approach holds promises for implementation in medical institutions with high complaint volumes and limited resources for addressing patient feedback.
Identification of a Susceptible and High-Risk Population for Postoperative Systemic Inflammatory Response Syndrome in Older Adults: Machine Learning–Based Predictive Model
Authors: Mai Haiyan , Lu Yaxin , Fu Yu , Luo Tongsen , Li Xiaoyue , Zhang Yihan , Liu Zifeng , Zhang Yuenong , Zhou Shaoli , Chen Chaojin 
Published: November 2024

Background
Systemic inflammatory response syndrome (SIRS) is a serious postoperative complication among older adult surgical patients that frequently develops into sepsis or even death. Notably, the incidences of SIRS and sepsis steadily increase with age. It is important to identify the risk of postoperative SIRS for older adult patients at a sufficiently early stage, which would allow preemptive individualized enhanced therapy to be conducted to improve the prognosis of older adult patients. In recent years, machine learning (ML) models have been deployed by researchers for many tasks, including disease prediction and risk stratification, exhibiting good application potential.

Objective
We aimed to develop and validate an individualized predictive model to identify susceptible and high-risk populations for SIRS in older adult patients to instruct appropriate early interventions.

Methods
Data for surgical patients aged ≥65 years from September 2015 to September 2020 in 3 independent medical centers were retrieved and analyzed. The eligible patient cohort in the Third Affiliated Hospital of Sun Yat-sen University was randomly separated into an 80% training set (2882 patients) and a 20% internal validation set (720 patients). We developed 4 ML models to predict postoperative SIRS. The area under the receiver operating curve (AUC), F1 score, Brier score, and calibration curve were used to evaluate the model performance. The model with the best performance was further validated in the other 2 independent data sets involving 844 and 307 cases, respectively.

Results
The incidences of SIRS in the 3 medical centers were 24.3% (876/3602), 29.6% (250/844), and 6.5% (20/307), respectively. We identified 15 variables that were significantly associated with postoperative SIRS and used in 4 ML models to predict postoperative SIRS. A balanced cutoff between sensitivity and specificity was chosen to ensure as high a true positive as possible. The random forest classifier (RF) model showed the best overall performance to predict postoperative SIRS, with an AUC of 0.751 (95% CI 0.709-0.793), sensitivity of 0.682, specificity of 0.681, and F1 score of 0.508 in the internal validation set and higher AUCs in the external validation-1 set (0.759, 95% CI 0.723-0.795) and external validation-2 set (0.804, 95% CI 0.746-0.863).

Conclusions
We developed and validated a generalizable RF model to predict postoperative SIRS in older adult patients, enabling clinicians to screen susceptible and high-risk patients and implement early individualized interventions. An online risk calculator to make the RF model accessible to anesthesiologists and peers around the world was developed.