bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of treatment for diffuse large B-cell lymphoma using plasma D-dimer levels
Authors: Shao Ruonan , Meng Danchen , Gao Beibei , Zhang Lu , Meng Qingqi , Hou Yanqiu , Long Zhangbiao , Yan Jinsong , Shi Xiaofeng 
Published: June 2024

Abstract

Objective
It has been documented that D-dimer levels have potential utility as a measure of tumor activity in diffuse large B-cell lymphoma (DLBCL), however whether it can be used as a predictive marker of treatment outcome has not been established. This study means to retrospectively evaluate the role of D-dimer in prediction of treatment efficacy in patients with DLBCL.
Materials and methods
151 patients with newly diagnosed DLBCL were enrolled. Blood samples were taken from those patients during the initial visit to our hospital and again after two cycles of chemotherapy to measure D-dimer levels. The link between plasma D-dimer concentrations and patients' clinical characteristics was explored before and after treatment.
Results
Patients showed statistically different plasma D-dimer levels at initial consultation across the four treatment-response groups [ complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD)]. Those in CR group had significantly lower D-dimer levels at initial consultation than those in PR and PD groups (p < 0.05). After chemotherapy, the PR group had considerably lower D-dimers than the SD and PD groups did (p < 0.05). The D-dimer difference percentage was lower in PR group than that in PD group (p < 0.05).
Conclusion
Patients who respond well to chemotherapy typically exhibit lower D-dimer levels at the initial diagnosis. Those in the SD or PD groups usually experience a greater increase in D-dimer levels following chemotherapy. Consequently, variations in plasma D-dimer levels before and after treatment may offer valuable insights for evaluating the efficacy of chemotherapy treatment.
Vanished Livelihoods’: Reverberations of Civil War in Swat, Pakistan
Authors: Bhatti Haseeb , Lim Steven 
Published: June 2024

Abstract

This article specifically probes, how the Taliban occupation and subsequent civil war caused disaster, destruction and damages—which severely distressed sustainable household livelihoods in Swat region of Pakistan. This case study relies on primary data from a household survey conducted in five out of seven conflict affected sub-districts of Swat. The quantitative methodology consists of a two-step procedure. First, a k-means clustering tool validates the household classification in Swat. The clustering algorithm produces five robust clusters (i.e., k = 5). Second, we find asset base (human, physical, social, natural and financial) constraints in pursuing alternate high return and sustainable livelihood possibilities. This study reveals specific limitations in terms of human and financial capitals in the region. Nevertheless, physical capital’s enhancement and asset values, increase the likelihood of adopting high-income portfolios. Therefore, we highlight priority improvements in wide-ranging infrastructural necessities. We maintain that these factors possess strong potential to enhance household specific asset deficiencies and make household livelihood choices sustainable again. Such sustainability has potential to diminish their chances to sympathise and join militants once more. If not, they may come under their potent influence again—as widely circulated reports in media about violent incidents reveal these days.
Stochastic stem bucking using mixture density neural networks
Authors: Simon Schmiedel 
Published: June 2024

Abstract

Poor bucking decisions made by forest harvesters can have a negative effect on the products that are generated from the logs. Making the right bucking decisions is not an easy task because harvesters must rely on predictions of the stem profile for the part of the stems that is not yet measured. The goal of this project is to improve the bucking decisions made by forest harvesters with a stochastic bucking method. We developed a Long Short-Term Memory (LSTM) neural network that predicted the parameters of a Gaussian distribution conditioned on the known part of the stem, enabling the creation of multiple samples of stem profile predictions for the unknown part of the stem. The bucking decisions could then be optimized using a novel stochastic bucking algorithm which used all the stem profiles generated to choose the logs to generate from the stem. The stochastic bucking algorithm was compared to two benchmark models: A polynomial model that could not condition its predictions on more than one diameter measurement, and a deterministic LSTM neural network. All models were evaluated on stem profiles of four coniferous species prevalent in eastern Canada. In general, the best bucking decisions were taken by the stochastic LSTM models, demonstrating the usefulness of the method. The second-best results were mostly obtained by the deterministic LSTM model and the worst results by the polynomial model, corroborating the usefulness of conditioning the stem curve predictions on multiple measurements.
Evaluation of Epidermal and Dermal Wound Healing of Dexemedetomidine in Rat Model
Authors: Soylu Seçil , Sarıtaş Zülfükar Kadir , Sarıtaş Tuba Berra , Görücü Fatma , Bülbül Aziz , Demirel Hasan Hüseyin , Koç Yusuf 
Published: June 2024

Abstract

Wound healing, which is a complex and systematic process initiated by the body immediately after injury, is affected by many factors that can accelerate or delay this process. In this study, we aimed to evaluate the effects of dexmedetomidine on wound healing in rats. Our randomized controlled experimental study was conducted on a total of 16 male Wistar albino rats, divided into 8 control groups where the wound was covered only with sterile gauze and 8 experimental groups where dexmedetomidine was applied and then covered with sterile gauze and bandage. The wound areas of the rats were measured, histological evaluation and biochemical analysis were performed and, compared between groups. Our results showed that total antioxidant status (TAS) score was higher in the dexmedetomidine group and total oxidant status (TOS) score was higher in the control group. Interleukin-1, interleukin-6, tumor necrosis factor-α, hydroxyproline and epidermal growth factor scores of the groups did not show a statistically significant difference. Left lateral wound measurement of the dexmedetomidine group on the 1st and 3rd days was statistically wider than the left lateral wound measurement of the control group. There was a statistically significant decrease in wound sizes in all groups and on days 1-3-7-9.-12-15.-17.-21. The dexmedetomidine group had more pronounced reepithelialization and more intense collagen deposition compared to the control group. Based on our results, we concluded that dexmedetomidine may have an improving role in the wound healing process but more comprehensive studies are needed to evaluate the effects of dexmedetomidine on wound healing.
Toxoplasma gondii IgG Serointensity and Cognitive Function in Bipolar Disorder
Authors: Rensch Paul , Postolache Teodor T. , Dalkner Nina , Stross Tatjana , Constantine Niel , Dagdag Aline , Wadhawang Abhishek , Mohyudding Farooq , Lowryd Christopher A. , Josephb Joshua , Birner Armin , Fellendorf Frederike T. , Finner Alexander , Lenger Melanie , Maget Alexander , Painold Annamaria , Queissner Robert , Schmiedhofer Franziska , Smolle Stefan , Tmava-Berisha Adelina , Reininghaus Eva 
Published: June 2024

Abstract

Background
Alongside affective episodes, cognitive dysfunction is a core symptom of bipolar disorder. The intracellular parasite Toxoplasma gondii has been positively associated with both a diagnosis of bipolar disorder as well as poorer cognitive performance, across diagnostic boundaries.
Methods
A total of 76 participants with bipolar disorder were tested for Toxoplasma gondii -specific IgG and IgM antibodies and for cognitive performance with a neuropsychological test battery. Cognitive parameters were categorized into three cognitive domains (attention and processing speed, verbal memory, executive function). Statistical analysis of associations between continuous indicators of cognitive status as dependent variables in relationship to Toxoplasma gondii, included multivariate analyses of co-variance for seropositivity, and partial correlations with IgG serointensity in IgG seropositives. All analyses were controlled for age and premorbid IQ.
Results
In seropositives (n = 27), individual parameters in the verbal memory domain showed significant inverse partial correlations with IgG antibody levels. Cognitive functioning did not differ between IgG seropositive and seronegative individuals in any of the cognitive domains (n = 76). IgM positives (n = 7) were too few to be analyzed.
Conclusions
This investigation is the first to show an association between Toxoplasma gondii IgG serointensity and memory function in a well-diagnosed bipolar disorder sample. While further research is necessary, latent Toxoplasma gondii infections could represent a risk factor for functional decline for patients with bipolar disorder and Toxoplasma gondii serointensity in seropositives may emerge as a biomarker for personalized treatment.
Vesicoureteric reflux and urolithiasis in the paediatric population: causality, correlation or no connection at all?
Authors: Berger Christoph , Haid Bernhard , Oswald Josef 
Published: June 2024

Abstract

Purpose
To investigate the potential influence of vesicoureteral reflux (VUR) on stone formation in children by evaluating causality in terms of temporal coincidence, corresponding laterality, presence of UTI and stone composition.
Methods
Retrospective data analysis was conducted on pediatric patients with stones (group 1) and those diagnosed with VUR (group 2). Data comprises demographics, voiding cysto-urethrograms (VCUGs) stone characteristics, and UTI history.
Results
In Group 1 (73 stone patients, mean age 8.8 years), VUR was diagnosed in 26% of VCUGs (8/31), predominantly in children under 3 years. Only in a minority of 6%, VUR may be causally linked to infection-stones (2/31). In Group 2 (1758 VUR patients, mean age 3.6 years), 0.5% were found to have stones (9/1758), with VUR potentially contributing to stone formation in 0.34% of cases. VUR in stone patients was low grade, except one child with neurogenic bladder and bilateral grade 4 VUR.
Conclusion
An association between VUR and stone formation occurs in very young children and appears incidental rather than causal. Metabolic factors play a more significant role for stone formation. Children with stones need not routinely be subjected to a VCUG.
Medical Nutrition Therapy for critically ill patients in Intensive Care Unit: Assessment of nutritional risk and length of stay
Authors: Alimuddin Indrawaty , Taslim Nurpudji Astuti , Syauki Andi Yasmin , Rasyid Haerani , Aminuddin  , Bukhari Agussalim 
Published: June 2024

Abstract

Background and objectives: Critically ill patients are at high risk of malnutrition which is associated with poor outcomes, increased morbidity, mortality and length of hospitalization. The mNUTRIC score is a nutritional screening and assessment tool to evaluate low or high risk of malnutrition in hospitalized patients. This study was conducted to determine the relationship between the provision of medical nutrition therapy and the length of stay of patients Intensive Care Unit.
Methods: This study used a retrospective cohort study design on 250 critical patients hospitalized in the Intensive Care Unit in 2020 and 2021. Energy intake was recorded in the form of trophic energy and full energy in patient groups with low and high risk mNUTRIC scores and length of stay, then analyzed the relationship between energy and length of stay in the Intensive Care Unit.
Results: After analyzing patients with low risk of malnutrition, the results showed that energy intake < 600 calories had a length of stay in the ICU with a mean ± SD of 4.76 ± 2.47 days, while energy ≥ 600 calories in patients with a length of stay of 9.65 ± 6.79 days had significant results with a p value = 0.000. Similarly, in high risk malnutrition, the provision of energy intake < 600 calories and ≥ 600 calories, has significant results with a mean ± SD length of stay of 5.00 ± 3.14 days and 11.33 ± 7.81 days, respectively. It can be said that low risk malnutrition patients, both given energy intake < 600 calories and ≥ 600 calories, have a shorter length of stay in the ICU than high risk patients.
Conclusion: Medical nutrition therapy provided as trophic energy and full energy had an association with the length of hospitalization of critically ill patients, where the length of hospitalization was shorter in subjects with low risk compared to high risk.
Impact of Fiber-Containing Enteral Nutrition on Microbial Community Dynamics in Critically Ill Trauma Patients: A Pilot-Randomized Trial
Authors: Serbanescu Mara A. , Wright Mary C. , Arnold Jason , White James R. , Wischmeyer Paul E. 
Published: June 2024

Abstract

Background
Gut microbial dysbiosis in the intensive care unit (ICU) is common, and certain changes, such as expansion of Enterobacteriaceae and other microbes with high pathogenic potential (pathobionts), are associated with increased risk of infection and death. Enteral nutrition (EN) with prebiotic short-chain fructooligosaccharides (scFOS-EN) promotes growth of commensal microbes like Bifidobacterium and certain Firmicutes in non-critically ill patients, and thus may potentially mitigate pathobiont expansion in the ICU. Though widely available, the impact of scFOS-EN on the microbial landscape when initiated in the setting of ICU-associated dysbiosis is still unknown. To address this gap, we conducted a pilot randomized controlled trial (RCT) in critically ill trauma patients to assess effects of scFOS-EN versus a fiber-free formula (NF-EN) on microbial dynamics and ecological interactions.
Methods
In this double-blinded RCT at a level one trauma center, mechanically-ventilated ICU patients with severe trauma (excluding gastrointestinal involvement) were randomized to receive scFOS-EN or NF-EN for 10 days. Stool and oral samples were collected sequentially, and microbial communities were analyzed using 16S ribosomal RNA amplicon sequencing. Linear mixed models were used to assess longitudinal microbiota responses, and network approaches were applied to evaluate gut microbial interactions.
Results
A total of 57 stool and 88 oral samples were included from 7 NF-EN patients and 10 scFOS-EN patients. All patients received broad-spectrum antibiotics and derangements in gut microbiota were present at time of formula initiation. Compared to NF-EN, scFOS-EN was associated with an accelerated loss of Bifidobacterium (-0.6% relative abundance/day, p = .026), and Firmicutes (3.5% /day, p < .001), and expansion of numerous Bacteroidaceae. Further, only scFOS-EN patients demonstrated an expansion in pathobiont Enterobacteriaceae (0.3%/day, p = .003). Network analyses revealed that a unique alliance between co-occurring Enterobacteriaceae and Bacteroidaceae and increased competition may underly changes in the microbiota to scFOS-EN.
Conclusion
Our study suggests administration of scFOS-EN in severe dysbiosis may further deplete beneficial microbes (e.g., Bifidobacterium and Ruminococcaceae) while supporting more resilient colonizers such as Bacteroidaceae, potentially providing a context-dependent advantage to Enterobacteriaceae – pathobionts associated with harm in the ICU. Our findings underscore the importance of investigating ecological interactions in critically ill patients to inform the use of microbiota-targeted therapies.
Trial registration:
The trial was registered (ClinicalTrials.gov, NCT03153397) and approved by Duke Health Institutional Review Board (IRB Pro00081414).
Design of a multiplexed virtual classroom
Authors: Chávez Elizabeth Fonseca , Ruiz-Flores Leticia , Guerrero Nikté Norma Ocampo 
Published: June 2024

Abstract

The purpose of this research is to design a multiplexed virtual classroom model (a technique for combining two or more contents or activities so that a single teacher can help students learn a topic) and to address the issue of classroom management, where the teacher has many activities and little planning time. To determine which contents will be multiplexed, an analysis of learning strategies was conducted, and the strategy was named with the acronym POSTTIE. Consequently, the design of the multiplexed virtual classroom model is presented as a living, cyclical, feedback system that can be implemented by a single teacher or several teachers in different stages of planning, execution, and evaluation, using at least three strategies. To begin, we analyzed which learning strategies were reported by sixth-semester engineering students from a public university to use during their study time. The learning strategies were identified through a questionnaire with an open-ended question, where students were asked to classify their 10 responses. In another section, they listed only one word from those 10 responses to use the first method of natural semantic networks. The results obtained from the questionnaire were then reaffirmed through factor analysis as the second method. Subsequently, a comparison of the results of the applied analysis methods was conducted. Finally, eight learning strategies were named that coincided with both methods and served to validate the results. A quantitative factor analysis was conducted. The exploratory part of the method involved 111 participants; their responses were analyzed using Method 1 and ATLAS.ti© software. The confirmatory part involved 142 participants, and their responses were analyzed using Method 2 with dimension reduction via SPSS and the R language, with a confidence level of 70% and an error < 1.12%.
Exploring the Role of Dietary Fiber in Modulating Treatment Outcomes for Cancer Patients: A Topic Modeling Approach
Authors: Moomin Aliu , Sidik Abubakar Ibn , Sidique Gawusu , Tizumah Marizuk Waris , Adam Maridia Kunateh 
Published: June 2024

Abstract

The effects of both soluble and insoluble fibers on cancer progression, treatment efficacy, and patient outcomes have been explored. This study utilizedadvanced topic modeling techniques to analyze a wide range of academic literature on the role of dietary fibers in cancer treatment. It highlights the significant preventive and therapeutic benefits of dietary fibers, especially their influence on gastrointestinal health, immune system modulation, and inflammation reduction.
The study employed topic modeling approach to provide insights into the interactions of dietary fibers at the molecular and cellular levels, affecting cancerous tissues and patient metabolism associated with cancer treatment. This study highlights gaps in the current literature, such as insufficient details on the underlying mechanisms and the variable impacts of different fiber types on specific cancer treatments. It also identified gaps in diet and cancer research by elaborating on how dietary fibers interact with cancer treatments and assessing their potential to enhance treatment effectiveness and improve patient health. Key findings indicate that dietary fibers significantly reduce the risk of developing gastrointestinal cancers and boost the efficacy of cancer therapies by impacting cellular activities and genetic expression related to cancer. These findings suggest substantial benefits of incorporating dietary fibers into cancer treatment regimens, potentially improving patient recovery and survival. Future research should focus on experimental studies to verify these mechanisms and clinical trials to develop dietary guidelines for cancer patients.
Adsorptive removal of Cd (II) ions from wastewater using biosorbent of Tectona grandis leaves: kinetic, equilibrium and thermodynamic studies
Authors: negi Ankita 
Published: June 2024

Abstract

Heavy metal contaminants are naturally carcinogenic, poisonous, and persistent. In the elimination of harmful heavy metals from polluted water, developing a novel revolutionary biosorbent with high efficiency and low cost from native biomaterial is a difficult task. The utilization of Tectona grandis (teak) leaves to make a possible biosorbent (TGLB) for extracting Cd (II) ions from contaminated wastewater solutions was investigated. To validate the sorption capacity of TGLB, the characteristics of the biosorbent were studied using Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), point of zero charge (pHPZC), and Brunauer-Emmett-Teller (BET) textural analysis. pH, time, biosorbent dose, temperature, and metal ion concentration were all altered in the batch studies. 93.2% maximal elimination at ideal pH 5 was achieved for a 5 g biosorbent dose, a working solution with a purity of 10 mg/L after a 45 min contact duration. In order to validate the research, the experimental data were also subjected to single-factor analysis (One-way ANOVA) and t-test. The Langmuir sorption model (R2 > 0.999) and pseudo-second-order kinetic model (R2 > 0.998) were the best matches for experimental sorption outcomes. The desorption studies implied that TGLB can be utilized efficiently for three consecutive cycles. A study of the utmost adsorption capabilities of numerous untreated biomaterial-based residues revealed that Tectona grandis leaves biosorbent (TGLB) is an effective, cheap, ecologically and economically friendly biosorbent for application in the elimination of the heavy metals from contaminated aqueous solutions.
Machine Learning Method to estimate parameters of the GPD distribution: Applied to Lobo flows and Yzeron water levels
Authors: Cyrille Okou Gueï , Kolé Keita 
Published: June 2024

Abstract

The Peaks Over Threshold (POT) is an extreme value theory method, used in the management of natural disasters such as floods, economic and financial crisis. These disasters have considerable impacts on the environment and justify the extreme data analysis using the POT. The results showed that, under certain conditions, the excesses distribution can be approximated by a Generalized Pareto Distribution (GPD). One of the major difficulties of POT lies in the GPD distribution parameters estimation, particularly the selection of the optimal threshold.
In this paper, we propose a machine learning method to estimate the GPD distribution parameters. In the First step, we identify the candidate thresholds using the mrlplot graphic and determine the admissible thresholds by the cross-validation method. The admissible thresholds are those which minimize the squared error between the estimated and empirical quantilites of the training and test samples. The shape and scale parameters of the GPD distribution are estimated by the Modified Newton-Raphson numerical method by setting each candidate threshold of the training sample. This numerical method uses a penalized log likelihood function. In the second step, we select the optimal threshold among the admissible thresholds using the Kolmogorov- Smirnov goodness-of-fit test. The optimal threshold is the one which gives the best adequation based on the pvalue and the Kolmogorow statistic. The numerical results for sample sizes (100, 200, 500, 1000, 2000, 5000) showed the reduction in estimation bias of our method for large sample sizes. This technique is illustrated in a case study of Lobo flows and Yzeron water levels, a water resource of great importance in Cˆote d’Ivoire et France.