bluuebunny/crossref_metadata_2025_split huggingface dataset:
The distribution characteristics of PD-1 pathway related immune cells in esophageal cancer tissue and their relationship with prognosis
Authors: Kong Dehua , Gao Chunyan , Yang Yu , Yang Lu , Ma Ji , Tang Shimin , Mao Ying , Li Na , Li Yong 
Published: May 2024

Abstract

Purpose
Exploring the distribution characteristics of immune cells related to the programmed cell death receptor-1 (PD-1) pathway in esophageal cancer(EC) tissue and its relationship with prognosis.
Methods
Tissue samples of 236 patients with EC after surgery in our hospital from January 2016 to January 2021 were included to explore the correlation between 6 kinds of immunohistochemical indicators and clinical characteristics of patients. The Kaplan-Meier method and LOG-rank test to univariate analyzed the effect of IHC expression level on patient survival, and the clinical prediction model was constructed to evaluate and valate the prediction model.
Results
The expression levels of PD-1, PD-L1, FOXP3+, and CD25+were positively correlated with tumor infiltration depth and lymph node metastasis (P < 0.05); The expression levels of CD4+and CD8+were negatively correlated with tumor infiltration depth and lymph node metastasis (P < 0.05). Lymph node metastasis, high expression of PD-1, PD-L1, FOXP3+, and CD25+are independent risk factors affecting patient prognosis (P < 0.05). Patients with low expression of PD-1, PD-L1, FOXP3+, CD25+ and high expression of CD4+,CD8+ had better three-year survival rates (P < 0.001). The prediction model constructed based on influencing factors has good discrimination and accuracy.
Conclusion
The prediction model based on PD-1, PD-L1, FOXP3+, CD25+ expression levels and lymphocytes infiltration has a high predictive validity for the prognosis of patients with EC.
Disseminated Septic Arthritis caused by Ureaplasma urealyticum in an Immunocompromised Patient with Hypogammaglobulinemia after Rituximab Therapy
Authors: Oguni Kohei , Fukushima Shinnosuke , Otsuka Yuki , Soejima Yoshiaki , Kawaguchi Marina , Sazumi Yosuke , Fujimori Takumi , Iio Koji , Umakoshi Noriyuki , Yamada Kazuki , Hagiya Hideharu , Otsuka Fumio 
Published: May 2024

Abstract

Purpose
Ureaplasma urealyticum is a rare pathogen associated with septic arthritis that predominantly affects patients with hypogammaglobulinemia. Bacterial identification of fastidious organisms is challenging because they are undetectable by routine culture testing. To the best of our knowledge, this is the first report of septic arthritis induced by U. urealyticum infection in Japan.
Case description
We describe the case of a 23-year-old Japanese female with secondary hypogammaglobulinemia (serum immunoglobulin level <500 mg/dL), identified 8 years after treatment with rituximab. The patient presented with persistent fever and polyarthritis that were unresponsive to ceftriaxone and prednisolone. Contrast-enhanced computed tomography and gallium-67 scintigraphy revealed effusion and inflammation in the left sternoclavicular, hip, wrist, knee, and ankle joints. Although Gram staining and bacterial culture of the drainage fluid from the left hip joint were negative, the condition exhibited characteristics of purulent bacterial infection. The patient underwent empirical treatment with doxycycline, and her symptoms promptly resolved. Subsequent 16S ribosomal RNA (rRNA) gene sequencing of the joint fluid confirmed the presence of U. urealyticum, leading to the diagnosis of septic arthritis. Combination therapy with doxycycline and azithromycin yielded a favorable recovery from the inflammatory status and severe arthritic pain.
Conclusion
This case highlights U. urealyticum as a potential causative agent of disseminated septic arthritis, particularly in patients with hypogammaglobulinaemia. The 16S rRNA gene analysis proved beneficial for identifying pathogens in culture-negative specimens, such as synovial fluid, in suspected bacterial infections.
Animal models of xenograft-induced cachexia of patient-derived pancreatic cancer: exploring potential new biomarkers and treatment targets
Authors: Rossini Caio V.T. , Guimarães Lara M.F. , Bento Carolina A. , Alves-de-Moraes Luís B.C. , Jukemura Jose , Machado Marcel CC , Reis Eduardo M. , Lameu Claudiana 
Published: May 2024

Abstract

Background:
Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer often accompanied by cachexia, a condition marked by physical decline, anorexia, and reduced quality of life. Cachexia varies in severity, categorized as precachexia, cachexia, and refractory cachexia, impacting patient survival and wellbeing. To explore cancer cachexia, we developed two experimental models using PDAC fragments from patients P08 and P12.
Methods:
Female BALB/c nude mice received subcutaneous transplants of PDAC xenografts from these patients. We rigorously assessed animal phenotypes, metabolism, and behavior during tumor progression to validate these cachexia models.
Results:
Each patient-derived xenograft (PDX) displayed a distinct cachexia profile. Mice with P12-derived PDAC exhibited mild cachexia, showing reductions in muscle (15%) and brown adipose tissue (25%) mass, and decreased activity (35%), without significant weight loss or food intake changes. In contrast, P08-derived PDAC induced severe cachexia with pronounced losses in body weight (29%), muscle (38%), cardiac muscle (27%), and brown adipose tissue (60%), coupled with sustained strength decline (51%). Both models exhibited increased pro-inflammatory cytokine expression in tumor tissues and markers associated with muscle degradation.
Conclusions:
Our findings highlight those mice with patient-derived PDAC xenotransplants represent robust models for studying the diverse cachexia profiles observed in patients. These models reflect the heterogeneity of PDAC cachexia and can distinguish between different stages. Importantly, they unveil potential biomarkers like IL-1β, IL-6, P2Y2 receptor, and ASS1, indicative of cachexia severity. The models highlight cachexia's complexity and underscore the importance of personalized approaches in cancer treatment.
Cautiously optimistic about data-driven algorithms in paediatric critical care, nurses’ perspectives in low-resource settings: a pre-implementation and human-centred design study in Malawi
Authors: Rakers Margot , Mwale Daniel , de Mare Lieke , Chirambo Lezzie , Bierling Bart , Likumbo Alice , Langton Josephine , Team IMPALA Study , Chavannes Niels , Os Hendrikus van , Calis Job , Dellimore Kiran , Villalobos-Quesada María 
Published: May 2024

Abstract

Introduction
Paediatric critical care nurses face challenges in promptly detecting patient deterioration and delivering high-quality care, especially in low-resource settings (LRS). Patient monitors equipped with data-driven algorithms that integrate monitor and clinical data can optimise scarce resources (e.g. trained staff) offering solutions to these challenges. Poor algorithm output design and workflow integration are important factors hindering successful implementation. This study aims to explore nurses' perspectives to inform the development of a data-driven algorithm and user-friendly interface for future integration into a continuous monitoring system for critical care in LRS.
Methods
Human-centred design methods, including contextual inquiry, semi-structured interviews, and co-design sessions, were carried out at the high-dependency units of Queen Elizabeth Central Hospital and Zomba Central Hospital in Malawi. Triangulating these methods, and employing qualitative content analysis principles, we identified what algorithm could assist nurses and used co-creation methods to design a user interface prototype.
Results
Workflow observations demonstrated the effects of personnel shortages and limited monitor equipment availability for vital sign monitoring. Interviews emphasised the advantages of predictive algorithms in anticipating deterioration, underlining the need to integrate the algorithm’s output, the (constant) monitoring data, and the patient's present clinical condition. Nurses preferred a scoring system represented with familiar scales and colour codes. During co-design sessions, trust, usability and context specificity were emphasized as requirements for these algorithms. Four prototype components were examined, with nurses favouring explainable and transparent scores represented by colour codes and visual representations of score changes.
Conclusions
Nurses in LRS perceive that data-driven algorithms, especially for predicting patient deterioration, could improve the provision of critical care. This can be achieved by translating nurses' perspectives into design strategies, as has been carried out in this study. The lessons learned are summarised as actionable pre-implementation recommendations for the development and implementation of data-driven algorithms in LRS.
Optimality of reinforced concrete coupled shear wall using machine learning
Authors: Kumari Nivedita , Prasad Prahlad , Madhuri Seeram 
Published: May 2024

Abstract

A coupled Shear Wall is a unified system consisting of the connection of two individual shear walls with a connecting beam (coupling beam). The coupling beam plays an important role in the lateral load resistance of the coupled shear wall structure. This paper addresses the innovative approach to optimizing coupling beam dimensions by introducing data in machine learning. The data are collected through ETABS modelling of encompassing buildings of varying heights, i.e., 15, 20, 25, 30 stories, with and without shear walls, and coupled shear walls with coupling beams of different lengths, i.e., 1 m, 1.5m, and 2m, and different depths, i.e., 1.5m, 1.25m, 1 m, 0.8m, and 0.75m which is analysed by keeping the end to end distance of both shear wall and shear wall with coupled beam in order to make it economical. The parameters considered include displacement, drift, reinforcement quantity, and concrete volume collected through ETABS. A total of 68 models were analyzed. Analysis made through that, in all of the storey except in 30 storey the shear wall with coupling beam dimension, length of 2 m and depth of 1.25 m is most optimum model and in the case of 30 storey optimized model changes, the coupling beam with a length of 1.5 m and depth of 1.25 m perform best. On increasing storey, it can be deduced that coupled shear wall performs much better. Furthermore, the machine learning-trained model will provide the optimum dimension of the coupling beam if storey height is provided.
Longitudinal Network Changes and Phenoconversion Risk in Isolated REM Sleep Behavior Disorder
Authors: Eidelberg David , Tang Chris , Nakano Yoshikazu , Vo An , Nguyen Nha , Schindlbeck Katharina , Poston Kathleen , Gagnon Jean-François , Postuma Ronald , Niethammer Martin , Ma Yilong , Peng Shichun , Dhawan Vijay 
Published: May 2024

Abstract

Isolated rapid eye movement sleep behavior disorder (iRBD) is a prodromal syndrome for Parkinson’s disease (PD) and related 𝛼-synucleinopathies. We conducted a longitudinal imaging study of network changes in iRBD and their relationship to phenoconversion. Expression levels for the PD-related motor and cognitive networks (PDRP and PDCP) were measured at baseline, 2 and 4 years, along with dopamine transporter (DAT) binding. PDRP and PDCP expression increased over time, with higher values in the former network. While abnormal functional connections were identified initially within the PDRP, others bridging the two networks appeared later. A model based on the rates of PDRP progression and putamen dopamine loss predicted phenoconversion within 1.2 years in individuals with iRBD. In aggregate, the data suggest that maladaptive reorganization of brain networks takes place in iRBD years before phenoconversion. Network expression and DAT binding measures can be used together to assess phenoconversion risk in these individuals.
Dietary risk assessment of drinking water and fish from cultivated wetlands of Ndop
Authors: Nkwatoh Therese Ncheuveu , Fai Patricia Bi Asanga , Tchamba Martin Ngankam , Titaku Nokenyi Emmanuel 
Published: May 2024

ABSTRACT

This study evaluated pesticide exposure practices, and the potential health risks of drinking water and consuming fish from the cultivated wetlands of Ndop, Cameroon. Six hundred and twenty-six questionnaires were conveniently administered to farmers (≥ 26 years old) in a cross-sectional study to assess exposure practices and dietary risks. The Chi-square and Pearson correlation coefficients were used to establish relationships between variables. The PRIMET model was used to predict a worst-case dietary risk. The pesticide handling practices of 90% of farmers were inadequate. Chlorpyriphos, lambda-cyhalothrin, fipronil, and paraquat dichloride posed a possible dietary risk at recommended and applied doses, with chlorpyrifos having the highest exposure toxicity ratio (ETRdiet = 36.72). Paraquat dichloride, fipronil, and lambda-cyhalothrin posed a possible dietary risk at 26.3%, 58.3%, and 62.2% of their recommended concentrations, respectively. Remarkably, the dietary risk for cypermethrin was acceptable at 5.8 times its recommended dose (ETRdietR = 0.29). The significant positive correlation (p = 0.000) between PECfish and ETRdiet, suggests a possible health risk of consuming fish and drinking water harvested from the wetlands, thus the need for replacing pesticides posing possible risks at lower or recommended concentrations with less toxic alternatives and to train farmers on pesticide application practices.
Pilot scale removal of organic micropollutants by combining ozonation with powdered activated carbon
Authors: Lind Petter , Sauvignet Philippe , Flodin Jonatan , Bourghardt Charlotte , Bengtsson Simon 
Published: May 2024

ABSTRACT

Pilot experiments were conducted to assess the removal of organic micropollutants from biologically treated wastewater at a municipal wastewater treatment plant in Sweden. Powdered activated carbon (PAC) was combined with a low dose of ozone added directly before the PAC contact tank, enabling ozone to react simultaneously with the PAC and the wastewater contaminants. This combination of ozone and PAC was compared to PAC or ozone alone. The process, which included coagulation/flocculation and ballasted high-rate lamella sedimentation, proved to be both robust and efficient for the removal of micropollutants. The addition of 1.3 mg PAC/mg DOC resulted in a 73% average reduction of micropollutants. With the addition of 0.13 mg O3/mg DOC, the reduction increased to 83%, which was similar to the outcome with the addition of 2.6 mg PAC/mg DOC without ozone. The process configuration and operating conditions did not lead to any detectable bromate formation despite relatively high influent bromide levels. The estimated operating costs for combining PAC with a low dose of ozone were significantly lower than for applying a higher PAC dose. Thus, this way of combining PAC with ozone seems to have several advantages with respect to treatment efficiency and operation.
Machine learning-based ensemble model for groundwater quality prediction: A case study
Authors: Jose Annie , Yasala Srinivas 
Published: May 2024

ABSTRACT

Groundwater quality is vital for public health and environmental sustainability. As managing large datasets is challenging for traditional methods, this study combines the hidden Markov model (HMM) and the artificial neural network (ANN), a machine learning-based ensemble model to predict groundwater quality in Kanyakumari District, Tamil Nadu, India. In order to train the model, the acquired data is cleaned and normalized. HMM is used to find hidden patterns while the ANN architecture is used to forecast groundwater quality categories. Accuracy, precision, sensitivity, and F1-scores calculation are necessary to evaluate the model's performance. The effectiveness of the approach can be analyzed by k-fold cross-validation scores. The study demonstrates the effectiveness of the HMM–ANN approach in groundwater quality prediction with an accuracy of 97.41%. Thus, the research contributes to groundwater quality assessment by offering a unique methodology that facilitates informed decision-making for water resource management and environmental conservation.
Synthesis and characterization of faujasite zeolite membrane for selective enrichment of Arthrobacter sp. in synthetic wastewater
Authors: El-kordy Abderrazek , Kanzy Heba M. , Elgamouz Abdelaziz , Douma Mohamed , Mazouz Hamid , Kawde Abdel-Nasser , Tijani Najib 
Published: May 2024

ABSTRACT
This paper centers on the preparation and characterization of both a clay support and a faujasite zeolite membrane. Additionally, the study explores the development of bacterial media to assess the performance of these prepared membranes. The faujasite zeolite membrane was created using the hydrothermal method, involving the deposition of a faujasite layer to fine-tune the pore sizes of the clay support. The clay supports were crafted from clay which was sieved to particle size Φ ≤ 63 μm, and compacted with 3.0 wt.% activated carbon, then sintered at 1,000 °C. Distilled water fluxes revealed a decrease from 1,500 L m−2 h−1 to a minimum of 412 L m−2 h−1 after 180 min of filtration. Both membranes were characterized by XRF, XRD, FTIR, adsorption–desorption of nitrogen (N2), and SEM-EDS. PCR technique was used for the identification of the isolated Arthrobacter sp., and the retention of the bacteria on the clay support and the faujasite zeolite membrane were found to be 96 and 99%, respectively. The results showed that the faujasite zeolite membrane passed the clay support due to a narrow pore size of the faujasite zeolite membrane of 2.28 nm compared to 3.55 nm for the clay supports.
SGLT2i Alleviates Atherosclerosis by Inhibiting NHE1 Activation to
Protect against Macrophage Senescence Induced by Angiotensin II
Authors: Cao Yang , Wang Tianhui , Tian Qian , Zhang Shuping , Zhu Yuan , Wang Lan , Li Nannan , Lei Miao , Mo Xiangang 
Published: May 2024

Background::
Sodium-dependent glucose transporter (SGLT2) inhibitors (SGLT2i)
have been found to have anti-atherosclerotic effects in clinical treatment.

Objectives::
The aim of this study was to explore whether angiotensin II (Ang II) induces changes
in the expression of Na+/H+ exchanger of cytoplasmic membrane channel proteins (NHE1)
and SGLT2 in macrophages and whether dapagliflozin (DAPA), an SGLT2i, protects against
Ang II induced macrophage senescence by inhibiting NHE1 activation to alleviate Atherosclerosis
(AS).

Methods::
After intervention with DAPA plus gavage or feeding them a high-fat diet, the mice's
aortas were dissected, and oil red O staining was performed. Cell proliferation and toxicity detection,
western blot, immunofluorescence, and β-galactosidase staining methods were adopted
to detect cell activity, expressions of senescence-related genes, and number of senescent cells
after different concentrations of Ang II or DAPA or plasmid NHE1 were treated with
RAW264.7 cells.

Results::
(1) The formation of AS plaques in ApoE -/- mice showed a downward trend under
DAPA. (2) After the intervention of Ang II, the cell activity of RAW264.7 decreased, and the
expression of senescent cells and related genes increased. (3) Under the Ang II condition, the
expression of SGLT2 and NHE1 increased, and SGLT2, NHE1, and senescence-related genes
decreased with the addition of DAPA. (4) The expression of NHE1, senescent cells and related
genes decreased in RAW264.7 cells after DAPA treatment with plasmid NHE1 intervention.

Conclusion::
SGLT2i alleviates atherosclerosis by inhibiting NHE1 activation to protect against
macrophage senescence induced by Ang II.
Research on Alzheimer's Disease (AD) Involving the Use of In vivo and In vitro Models and Mechanisms
Authors: Sinha Sweta , Wal Pranay , Goudanavar Prakash , Divya Surisetti , Kimothi Vishwadeepak , Jyothi Divya , Sharma Mukesh Chandra , Wal Ankita 
Published: May 2024

Background:
Alzheimer's Disease (AD) is a neurodegenerative disorder characterized
by the progressive formation of extracellular amyloid plaques, intracellular neurofibrillary
tangles, inflammation, and impaired antioxidant systems. Early detection and intervention are
vital for managing AD effectively.

Objective:
This review scrutinizes both in-vivo and in-vitro screening models employed in Alzheimer's
disease research. In-vivo models, including transgenic mice expressing AD-related
mutations, offer profound insights into disease progression and potential therapeutic targets. A
thorough understanding of these models and mechanisms will facilitate the development of
novel therapies and interventions for Alzheimer's disease. This review aims to provide an overview
of the current experimental models in AD research, assess their strengths and weaknesses
as model systems, and underscore the future prospects of experimental AD modeling.

Methods:
We conducted a systematic literature search across multiple databases, such as Pub-
Med, Bentham Science, Elsevier, Springer Nature, Wiley, and Research Gate. The search strategy
incorporated pertinent keywords related to Alzheimer's disease, in-vivo models, in-vitro
models, and screening mechanisms. Inclusion criteria were established to identify studies focused
on in-vivo and in-vitro screening models and their mechanisms in Alzheimer's disease research.
Studies not meeting the predefined criteria were excluded from the review.

Results:
A well-structured experimental animal model can yield significant insights into the
neurobiology of AD, enhancing our comprehension of its pathogenesis and the potential for
cutting-edge therapeutic strategies. Given the limited efficacy of current AD medications, there
is a pressing need for the development of experimental models that can mimic the disease, particularly
in pre-symptomatic stages, to investigate prevention and treatment approaches. To address
this requirement, numerous experimental models replicating human AD pathology have
been established, serving as invaluable tools for assessing potential treatments.

Conclusion:
In summary, this comprehensive review underscores the pivotal role of in-vivo and
in-vitro screening models in advancing our understanding of Alzheimer's disease. These models
offer invaluable insights into disease progression, pathological mechanisms, and potential therapeutic
targets. By conducting a rigorous investigation and evaluation of these models and
mechanisms, effective screening and treatment methods for Alzheimer's disease can be devised.
The review also outlines future research directions and areas for enhancing AD screening models.