bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lower Geriatric Nutritional Risk Index Are Associated with A Higher Incidence Of Osteoporosis In Northern China Type 2 Diabetes
Authors: Ji Yuan yuan , Geng Nan , niu ying chun , Zhao Hang , fei wen jie , Chen Shu chun , Ren Lu ping 
Published: August 2022

Abstract
Background Osteoporosis (OP) is very common in the elderly population and can lead to fractures and disability. Malnutrition is associated with osteoporosis. The Geriatric Nutrition Risk Index (GNRI) is used to assess the risk of malnutrition and complications associated with nutritional status in older patients and is an important predictor of many diseases. Therefore, we investigated the relationship between GNRI and osteoporosis in patients with type 2 diabetes to help us prevent and detect osteoporosis in time. Methods Retrospective study of 610 elderly patients with type 2 diabetes. Collection of general and laboratory data on the patient, together with measurement of bone mineral density (BMD). The GNRI was calculated based on ideal body weight and serum albumin (ABL) levels. Correlation analysis was applied to determine the relationship between GNRI and BMD and bone metabolism indexes. The predictive value of GNRI for the development of osteoporosis was analyzed by logistic regression analysis and creating a Receiver Operating Characteristic Curve (ROC), calculating the area under the cure (AUC).Results All patients divided into the no nutritional risk group and the nutritional risk group. Compared with the non-nutritional risk group, the nutritional risk group had longer diabetes course, older age, higher HbA1c and higher prevalence of osteoporosis; lower BMI, ABL, FCP, TG, Ca, 25 (OH) D, PTH and lower femoral neck BMD (P<0.05). Classified in ascending order of GNRI quartiles (Q1-Q4), lower GNRI among type 2 diabetic patients are associated with a higher Incidence of osteoporosis. All patients were grouped with non-osteoporosis group and osteoporosis group. Patients in the non-osteoporotic group had higher GNRI values compared to the osteoporotic group(P<0.05). Correlation analysis revealed that GNRI was positively correlated with lumbar BMD, femoral neck BMD and total hip BMD (P<0.05). After adjusting confounding factors, Spearman's correlation analysis was concluded that GNRI was positively correlated with Ca, 25(OH)D, PTH and negatively correlated with ALP and PINP. Regression analysis yields, GNRI was significantly associated with osteoporosis. ROC curve analysis was performed with GNRI as the test variable and the presence of osteoporosis as the status variable. The analysis yielded an area under the curve for GNRI of 0.695 and was statistically significant (P< 0.05), with an optimal GNRI threshold of 99.56 for predicting osteoporosis.Conclusions: Lower Geriatric Nutritional Risk Index among type 2 diabetic patients in northern China are associated with a higher Incidence of osteoporosis.
When in Doubt, Map it Out: Teachers’ Digital Storytelling Researched through Documentation
Authors: Lemieux Amélie , Mason Stephanie 
Published: August 2022

This article considers how documentation enriches literacies learning in higher education, specifically in a graduate course designed for language teachers. Building on a one-year research study with graduate students at a university in the Atlantic region of Canada, the authors demonstrate how participant-generated documentation, including cartography, presents relational understandings impacting literacies. Specifically, the authors look at a case study of two teachers enrolled in a graduate literacy course who crafted and designed digital stories using Scratch and used multimodal dimensions from music to animation and movement. Teachers’ documentation challenges the idea that making is solely a question of doing, and considers instead long-lasting processes that influence teacher practice and development.
Learning, Technology, and Technique
Authors: Dron Jon 
Published: August 2022

To be human is to be a user, a creator, a participant, and a co-participant in a richly entangled tapestry of technologies – from computers to pedagogical methods - that make us who we are as much as our genes. The uses we make of technologies are themselves, nearly always, also technologies, techniques we add to the entangled mix to create new assemblies. The technology of greatest interest is thus not any of the technologies that form that assembly, but the assembly itself. Designated teachers are never alone in creating the assembly that teaches. The technology of learning almost always involves the co-participation of countless others, notably learners themselves but also the creators of systems, artifacts, tools, and environments with and in which it occurs. Using these foundations, this paper presents a framework for understanding the technological nature of learning and teaching, through which it is possible to explain and predict a wide range of phenomena, from the value of one-to-one tutorials, to the inadequacy of learning style theories as a basis for teaching, and to see education not as a machine made of methods, tools, and systems but as a complex, creative, emergent collective unfolding that both makes us, and is made of us.
Features of the course of atopic dermatitis with facial and neck lesions (head and neck)
Authors: Tamrazova O. B. , Glukhova E. A. , Tamrazova A. V. 
Published: August 2022

Dermatitis of head and neck, also known as “head-and-neck dermatitis” (HNAD), is one of the specific manifestations of atopic dermatitis (AD) in adolescents and young adults. The epidermal barrier dysfunction, skin pathological immune responses, a direct damaging effect of the malassezial flora through the synthesis of virulence factors and mediated by maintaining immune inflammatory responses, as well as the attachment of secondary bacterial flora caused by scratching form the basis of pathogenetic mechanisms of this condition. The properties that define the HNAD rash is a focus on the seborrheic areas (a face, front surface of the neck, décolleté on the chest), severe itching, prolonged course, exacerbation characterized by excessive sweating. It appears that fungi of the genusMalasseziacan play a role in the development of the disease. While they are considered to be part of the normal skin microbiome, a significant increase in the severity of atopic dermatitis was observed in patients with Malassezial flora, which was the reason for the theories devoted to the role ofMalassezia spp. as a trigger of the disease. Atopic dermatitis can also develop as a side effect of using targeted therapy with IL4/13 blockers, which is explained at the present day by a shift of the immune response towards a Th-17-mediated reaction. The combination of both infectious and immune-mediated lesions in HNAD determines the necessity of an integrated approach to the therapy, in particular the use of topical combination drugs as the first-line therapy. The topical glucocorticosteroid (TGCS) therapy is aimed at suppressing the immune reactions that are responsible for inflammation in the skin, antifungal therapy is required to suppress the activity of the malassesial flora, and, finally, a secondary bacterial infection requires the prescription of topical antibacterial drugs.
<I>TNF-α</I> gene expression in immune cells of patients with psoriasis and psoriatic arthritis
Authors: Sobolev V. V. , Chebysheva S. N. , Geppe N. A. , Katkova K. V. , Soboleva A. G. , Korsunskaya I. M. 
Published: August 2022

Introduction. Psoriatic disease is a heterogeneous inflammatory disease with different clinical manifestations, including plaque psoriasis and psoriatic arthritis. It has been shown that elevated levels of TNF-α are observed in both psoriatic arthritis and psoriasis. Studying the TNF-α gene expression pattern can help in the differential diagnosis between psoriatic arthritis and psoriasis.The objective is to study the TNF-α gene expression pattern in blood mononuclear cells of patients with psoriatic arthritis and psoriasis for possible differential diagnosis between these two diseases.Materials and methods. Mononuclear cells were isolated from the peripheral blood of 31 patients with plaque psoriasis, 45 patients with psoriatic arthritis and 20 healthy controls. The expression level of the TNF-α gene was analysed using a real-time PCR method.Results and discussion. As a result of the comparison, the expression level of TNF-α in patients with psoriatic arthritis was found to be 179 times higher than the expression level in healthy volunteers. The expression level of TNF-α in patients with psoriasis was also significantly (106 times) higher than the expression level in healthy people. We managed to identify a significant difference between patients with psoriatic arthritis and psoriasis.Conclusions. Patients with psoriasis in terms of TNF-α gene expression level in mononuclear cells are close to the condition of patients with psoriatic arthritis. A high level of TNF-α gene expression can become a marker of possible joint injury in patients with psoriasis and a signal to revise the therapeutic approach to a particular patient.
Runoff estimation using digital image processing for residential areas
Authors: Soni Pramod , Medhi Hemanta , Sagar Anitya , Garg Pulkit , Singh Abhay , Karna Umesh 
Published: August 2022

Abstract
With the burgeoning population worldwide, the demand for freshwater supply is increasing, mostly in urban areas, due to the influx of people for better livelihood. To mitigate this burden of freshwater demand and build a sustainable water management system, harvesting rainwater during the rainfall season is a viable option. Runoff estimation studies in the past are time-intensive as parameter estimation for an area is complex by the conventional method. In this study, the Motilal Nehru National Institute of Technology (MNNIT), Allahabad campus was selected as a pilot project to assess a methodology that uses Google Earth images for obtaining the runoff coefficients. This method is easy and consumes less time in runoff estimation. This was compared with the conventional method. Using the conventional method (Arc-GIS), the equivalent runoff coefficients for these catchments were found to be 0.2780, 0.3553, and 0.4111, respectively. The range of error (compared to the traditional method) in runoff obtained from the proposed method with a default k value (0.8) was found to be 8.16–13.55%, with an average value of 9.91%. However, with a slightly modified value of k (0.9), the errors were significantly reduced to 1.94–3.32%, with an average of 2.15%.
Application of biochar as an innovative soil ameliorant in bioretention system for stormwater treatment: A review of performance and its influencing factors
Authors: Chen Yao , Wu Qiong , Tang Yinghui , Liu Zhen , Ye Lilan , Chen Renyu , Yuan Shaochun 
Published: August 2022

Abstract
As an emerging environment functional material, biochar has become a research hotspot in environmental fields because of its excellent ecological and environmental benefits. Recently, biochar has been used as an innovative soil ameliorant in bioretention systems (BRS) to effectively enhance pollutant removal efficiency for BRS. This paper summarizes and evaluates the performance and involved mechanisms of biochar amendment in BRS with respect to the removal of nutrients (TN (34–47.55%) and PO43--P (47–99.8%)), heavy metals (25–100%), pathogenic microorganisms (Escherichia coli (30–98%)), and organic contaminants (77.2–100%). For biochar adsorption, the pseudo-second-order and Langmuir models are the most suitable kinetic and isothermal adsorption models, respectively. Furthermore, we analyzed and elucidated some factors that influence the pollutant removal performance of biochar-amended BRS, such as the types of biochar, the preparation process and physicochemical properties of biochar, the aging of biochar, the chemical modification of biochar, and the hydraulic loading, inflow concentration and drying–rewetting alternation of biochar-amended BRS. The high potential for recycling spent biochar in BRS as a soil ameliorant is proposed. Collectively, biochar can be used as an improved medium in BRS. This review provides a foundation for biochar selection in biochar-amended BRS. Future research and practical applications of biochar-amended BRS should focus on the long-term stability of treatment performances under field conditions, chemical modification with co-impregnated nanomaterials in biochar surface, and the durability, aging, and possible negative effects of biochar.
Biodegradation characteristics and mechanism of quinoline by Ochrobactrum sp. strain C2
Authors: Zhao Qiancheng , Hu Qiaoyu , Qiu Ziliang , Yu Caihong 
Published: August 2022

Abstract
A quinoline-degrading strain, C2, which could completely degrade 250 mg/L of quinoline within 24 h, was isolated from coking wastewater. Strain C2 was identified as Ochrobactrum sp. on the basis of 16S rDNA sequence analysis According to 16S rDNA gene sequence analysis, Strain C2 was identified as Ochrobactrum sp. Strain C2 could utilize quinoline as the sole carbon sources and nitrogen sources to grow and degrade quinoline well under acidic conditions. The optimum inoculum concentration, temperature and shaking speed for quinoline degradation were 10%, 30 °C and 150 r/min, respectively. The degradation of quinoline at low concentration by the strain followed the first-order kinetic model. The growth process of strain C2 was more consistent with the Haldane model than the Monod model, and the kinetic parameters were: Vmax = 0.08 h−1, Ks = 131.5 mg/L, Ki = 183.1 mg/L. Compared with suspended strains, strain C2 immobilized by sodium alginate had better degradation efficiency of quinoline and COD. The metabolic pathway of quinoline by Strain C2 was tentatively proposed, quinoline was firstly converted into 2(1H) quinolone, then the benzene ring was opened with the action of catechol 1,2-dioxygenase and subsequently transformed into benzaldehyde, 2-pentanone, hydroxyphenyl propionic acid and others.
Base-Promoted Regioselective Bromination of Imidazo[1,2-α]pyridines
with CBr4 as Bromine Source
Authors: Zhang Fengmei , He Pei , Liu Chunbo , Shen Qinpeng , Xia Jianjun , Han Ying , Xiong Wen , Li Zhenjie , Tang Shiyun , Liu Zhihua 
Published: August 2022

Abstract:
A bromination of hydrocarbons with CBr4 as a bromine source, promoted by NaOH, has
been developed. The reaction method with high efficiency and regioselectivity for the synthesis of
C3-brominated imidazo[1,2-α]pyridines. An effective and simple method of C-X bond construction has
been developed, which is a supplement to the halide of imidazo[1,2-α]pyridines.
Dexamethasone Loaded Electrospun Nanocomposite Ocular Insert:
In-vitro Drug Release and Mechanical Assessment
Authors: Benival Derajram , Agarwal Rishabh , Rana Dhwani , Salave Sagar 
Published: August 2022

Background:
Dexamethasone is currently available as suspension for the treatment of anterior ocular inflammatory diseases that are given through eye drops. Upon topical delivery by eye-drops, less than 5% of the drug gets absorbed when applied topically as eye drops. The major portion of administered drug either comes out from the eye or gets subjected to nasolacrimal drainage resulting in poor bioavailability.

Objective:
The present study is aimed at developing polymeric ocular insert containing dexamethasone as a drug by employing the use of electrospinning technique.

Methods:
Dexamethasone (0.1% w/v) loaded electrospun sheet was also made using 10% w/v EC: HPMC: PEO (60:20:20) in solvent system of ethanol: water (90:10) at optimized electrospinning parameters of 12kV with a flow rate of 0.8mL/hr and distance of 20 cm between tip and collector.

Results:
The prepared nanocomposite insert was characterized for DSC and FTIR-ATR spectroscopy revealing no physical-chemical interaction between drug with polymers. The thickness of the electrospun sheet was found to be 270±0.02 μm and % drug content was found to be 0.43±0.01% w/w. The release profile showed that around 95% drug was released in 48 hrs. This release profile showed the prepared drug-loaded electrospun ocular insert was best suitable for once-a-day delivery. Assessment of mechanical properties like young’s modulus, tensile strength, and % elongation showed that the prepared insert can be handled easily without any breakage or damage.

Conclusion:
Upon delivery in conjunctival sac, this is best suitable for once-a-day delivery. The developed electrospun insert, consisting of a unique polymer composite of biodegradable polymers, avails the benefits of nanofibers imparting prolonged release and this novel formulation overcomes the limitations of conventional therapies. This reduces the dosing frequency and improves patient compliance.
Racial Disparities in Patient-Provider Communication During Telehealth Visits Versus Face-to-face Visits Among Asian and Native Hawaiian and Other Pacific Islander Patients With Cancer: Cross-sectional Analysis
Authors: Acoba Jared D , Yin Chelsea , Meno Michael , Abe Justin , Pagano Ian , Tamashiro Sharon , Fujinaga Kristy , Braun-Inglis Christa , Fukui Jami 
Published: August 2022

Background
Telehealth visits increase patients’ access to care and are often rated as “just as good” as face-to-face visits by oncology patients. Telehealth visits have become increasingly more common in the care of patients with cancer since the advent of the COVID-19 pandemic. Asians and Pacific Islanders are two of the fastest growing racial groups in the United States, but there are few studies assessing patient satisfaction with telemedicine among these two racial groups.

Objective
Our objective was to compare satisfaction with communication during telehealth visits versus face-to-face visits among oncology patients, with a specific focus on Asian patients and Native Hawaiian and other Pacific Islander (NHOPI) patients.

Methods
We surveyed a racially diverse group of patients who were treated at community cancer centers in Hawaii and had recently experienced a face-to-face visit or telehealth visit. Questions for assessing satisfaction with patient-physician communication were adapted from a previously published study of cancer survivors. Variables that impact communication, including age, sex, household income, education level, and cancer type and stage, were captured. Multivariable logistic models for patient satisfaction were created, with adjustments for sociodemographic factors.

Results
Participants who attended a face-to-face visit reported higher levels of satisfaction in all communication measures than those reported by participants who underwent a telehealth encounter. The univariate analysis revealed lower levels of satisfaction during telehealth visits among Asian participants and NHOPI participants compared to those among White participants for all measures of communication (eg, when asked to what degree “[y]our physician listened carefully to you”). Asian patients and NHOPI patients were significantly less likely than White patients to strongly agree with the statement (P<.004 and P<.007, respectively). Racial differences in satisfaction with communication persisted in the multivariate analysis even after adjusting for sociodemographic factors. There were no significant racial differences in communication during face-to-face visits.

Conclusions
Asian patients and NHOPI patients were significantly less content with patient-physician communication during telehealth visits when compared to White patients. This difference among racial groups was not seen in face-to-face visits. The observation that telehealth increases racial disparities in health care satisfaction should prompt further exploration.
The Impact of Time Horizon on Classification Accuracy: Application of Machine Learning to Prediction of Incident Coronary Heart Disease
Authors: Simon Steven , Mandair Divneet , Albakri Abdel , Fohner Alison , Simon Noah , Lange Leslie , Biggs Mary , Mukamal Kenneth , Psaty Bruce , Rosenberg Michael 
Published: August 2022

Background
Many machine learning approaches are limited to classification of outcomes rather than longitudinal prediction. One strategy to use machine learning in clinical risk prediction is to classify outcomes over a given time horizon. However, it is not well-known how to identify the optimal time horizon for risk prediction.

Objective
In this study, we aim to identify an optimal time horizon for classification of incident myocardial infarction (MI) using machine learning approaches looped over outcomes with increasing time horizons. Additionally, we sought to compare the performance of these models with the traditional Framingham Heart Study (FHS) coronary heart disease gender-specific Cox proportional hazards regression model.

Methods
We analyzed data from a single clinic visit of 5201 participants of a cardiovascular health study. We examined 61 variables collected from this baseline exam, including demographic and biologic data, medical history, medications, serum biomarkers, electrocardiographic, and echocardiographic data. We compared several machine learning methods (eg, random forest, L1 regression, gradient boosted decision tree, support vector machine, and k-nearest neighbor) trained to predict incident MI that occurred within time horizons ranging from 500-10,000 days of follow-up. Models were compared on a 20% held-out testing set using area under the receiver operating characteristic curve (AUROC). Variable importance was performed for random forest and L1 regression models across time points. We compared results with the FHS coronary heart disease gender-specific Cox proportional hazards regression functions.

Results
There were 4190 participants included in the analysis, with 2522 (60.2%) female participants and an average age of 72.6 years. Over 10,000 days of follow-up, there were 813 incident MI events. The machine learning models were most predictive over moderate follow-up time horizons (ie, 1500-2500 days). Overall, the L1 (Lasso) logistic regression demonstrated the strongest classification accuracy across all time horizons. This model was most predictive at 1500 days follow-up, with an AUROC of 0.71. The most influential variables differed by follow-up time and model, with gender being the most important feature for the L1 regression and weight for the random forest model across all time frames. Compared with the Framingham Cox function, the L1 and random forest models performed better across all time frames beyond 1500 days.

Conclusions
In a population free of coronary heart disease, machine learning techniques can be used to predict incident MI at varying time horizons with reasonable accuracy, with the strongest prediction accuracy in moderate follow-up periods. Validation across additional populations is needed to confirm the validity of this approach in risk prediction.