bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design, operation and strengthening of health practitioner regulation systems: A rapid integrative review
Authors: Leslie Kathleen , Bourgeault Ivy Lynn , Carlton Anne-Louise , Balasubramanian Madhan , Mirshahi Raha , Short Stephanie , Carè Jenny , Cometto Giorgio , Lin Vivian 
Published: December 2022

Abstract
Background: In addition to promoting patient safety, health practitioner regulation (HPR) systems are increasingly recognized as playing an important role in supporting health workforce availability, accessibility, quality, and sustainability. This review aimed to identify the evidence on the design, delivery and effectiveness of HPR systems to inform policy decisions by policymakers and regulators.
Methods: A rapid review approach was adopted to conduct an integrative analysis of literature published between 2010 and 2021. Fourteen databases were searched according to a standardized strategy. Data were extracted and synthesized through an integrative approach and categorized according to a modified Donabedian framework.
Results We identified 410 academic and 426 grey literature sources. Key themes included HPR structures (regulatory governance systems, regulatory institutions, and system linkages), processes (registration and monitoring of continuing competence of practitioners, accreditation of entry-to-practice education programs, regulating scopes of practice, managing complaints and discipline, and regulating traditional and complementary medicine) and outcomes (impact of HPR structures and processes on health system and workforce outcomes). More evidence was found on HPR structures and processes than outcomes, though over 99% of studies were descriptive (and evidence, accordingly, of very low certainty). Synthesis was constrained by the lack of common terminology.
Discussion: Trends on regulatory structures and processes emerged from this analysis. The evidence base limitations, particularly on HPR outcomes, warrant caution in the interpretation, generalizability, and applicability of these findings. Standardized terminology and more evidence on regulatory outcomes (on both workforce and health systems) could inform decisions on regulatory design and implementation.
A Novel Immune-Related Prognostic Model Based on Chemosensitivity Predicts the Outcome of Ovarian Cancer
Authors: Wang Yuhua , Zhang Yifan , Li Dingxi , Wang Li 
Published: December 2022

Abstract
Background: Ovarian cancer (OC) is the most lethal gynecological tumor. Chemotherapy resistance is a significant factor in the treatment and prognosis of ovarian cancer (OC). Compelling evidence indicates that changes in tumor immune microenvironmental are crucial to chemotherapy responses. Here, we aimed to construct an immune-related gene pairs classifier base on the chemosensitivity status of OC.
Methods:
Gene expression and clinical data collected from The Cancer Genome Atlas (TCGA) were to screen immune- and chemosensitivity-related genes. By univariate analysis and least absolute shrinkage and selection operator (LASSO) cox analysis, gene pairs associated with prognosis were identified from the intersection of the two parts of the genes. The prognostic signature was constructed by multivariate analysis. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves were applied to evaluate the predictive validity of the risk model in independent Gene Expression Omnibus (GEO) datasets. A nomogram containing the risk signature and clinical characters was constructed. Besides, we appraised the forecasting capability of prognostic signature in clinicopathological features, immune landscape, gene mutation, the efficacy of immunotherapy, and drug sensitivity. The potential molecular mechanism of the signature was investigated by gene set enrichment analysis (GSEA).
Results: The prognostic signature consisting of eleven chemosensitivity- and immune-related gene pairs was constructed in our study. The risk score, age, and chemosensitivity could be independent predictors for overall survival (OS). Nomogram and ROC curves demonstrate the importance of risk score and provide personal mortality risk prediction at different time points. The calibration plot shows the reliability of the nomogram. In addition, patients in the high-risk group had a lower IC50 for several common agents. In terms of the immune microenvironment, we found that B cells memory, T cells CD4 memory activated, and dendritic cells activated higher degree of infiltration in the low-risk group whereas it was the opposite for T cells CD4 naive, T cells CD4 memory resting, and M2 macrophages. Patients with high-risk scores had elevated expression of immune checkpoint genes, speculating that these patients may be more suitable for immunotherapy. Function analysis also confirmed our findings that the risk model may provide new targets for precision immunotherapy.
Conclusions: Our study developed a chemosensitivity- and immune-related prognostic model to predict the prognosis of ovarian cancer patients, providing new sights in optimizing patient selection to improve future outcomes.
An Ultra-rapid and Green Method for Simultaneous Determination of Honokiol and Magnolol in Magnoliae Officinalis Cortex with One Standard by HPLC-UV at Equal Absorption Wavelength
Authors: Qian Zhengming , Chen Jing , Lei Qinggui , Tan Guoying , Zou Yuansheng , Peng Gang , Li Wenqing , Xie Juying 
Published: December 2022

Abstract

An ultra-rapid and green assay method for simultaneous determination of honokiol and magnolol in Magnoliae Officinalis Cortex with one standard was developed by HPLC-UV at equal absorption wavelength. The sample was prepared by ultrasonic-assisted matrix solid-phase dispersion. The HPLC separation was performed on a Poroshell C18 column with an eco-friendly mobile phase. The detection wavelength was set at the equal absolution wavelength of honokiol and magnolol (247 nm). The contents of honokiol and magnolol in six batches of samples, obtained by developed method with one marker and external standard method with two markers, were comparable. In addition, the developed HPLC method only took 2.5 min and 4.55 mL green organic solution (ethanol), which including the sample extraction and separation. The developed method was rapid, green and standard saving, which would be helpful to improve the quality evaluation of Magnoliae Officinalis Cortex.
Identify the key ecosystem service providing areas for climate change adaptation in a low mountain region of Germany
Authors: Al-Qubati Abdulhakeem , Zhang Lulu , Pyarali Karim 
Published: December 2022

Abstract

The frequency of extreme weather events has increased in the latest years. The recent droughts in Europe caused severe damage in many sectors and underlined the need for adaptation. This paper aims to support decision-making for climate adaptation in a low-mountainous region of central Germany by identifying the areas and ecosystems that are susceptible and significantly impacted by droughts. We analysed temperature and precipitation trends and drought conditions in the Weisse Elster watershed. The temperature increased on both seasonal and annual scales, while precipitation decreased in some areas in the summertime and increased in the wintertime and annual scale. The standardised precipitation-evapotranspiration index (SPEI) showed worsening drought conditions, especially after the late 1980s. Applying an ecosystem services (ES) model, the response of two key ES (surface water provision and carbon sequestration) to droughts is estimated. To identify the vulnerable areas and ecosystems to droughts, the simulated spatially averaged water yield, net ecosystem productivity (NEP), and soil moisture are compared for the five worst droughts with long-term averages. Droughts caused a reduction of water yield by 54%, NEP by 18% and upper zone soil moisture by 13%. The impacts varied spatially, with the central region being worst affected while the southern region was relatively more resilient. There is an urgent need for adaptation measures to reduce the risks of droughts and enhance climate resilience. Suggestions are proposed for the adaptation of the agriculture and forest sectors.
SNHG18 inhibits bladder cancer cell proliferation by increasing p21 transcription through destabilizing c-Myc protein
Authors: Ke Meixia , Sun Ning , Lin Zhenni , Zhang Peipei , Hu Yan , Wu Shuilian , Zheng Zhijian , Lu Yongyong , Jin Honglei Jin 
Published: December 2022

Abstract

Background Long non-coding RNAs (lncRNAs) have been confirmed to play important roles in various cancers including bladder cancer (BC). The precise expression pattern of lncRNA small nucleolar RNA host gene 18 (SNHG18) in BC and its mechanisms of action have not been fully explored. Materials and methods The expression of SNHG18 was evaluated by RT-qPCR in bladder cancer clinical samples and human bladder cancer cell lines, and stable cell lines overexpressing SNHG18 were constructed. The effect of SNHG18 on the proliferation of bladder cancer cells was detected by soft agar colony formation test, ATP activity test and subcutaneous tumorigenesis model in nude mice. The specific mechanism of SNHG18 inhibition of bladder cancer proliferation was studied by flow cytometry, western blotting, dual luciferase reporter gene assay and protein degradation assay. Results We found that SNHG18 is significantly downregulated in BC tissues and cell lines. Kaplan–Meier analysis showed that SNHG18 expression is positively correlated with survival in BC patients. Ectopic overexpression of SNHG18 significantly inhibited the proliferation of BC cells in vitro and in vivo. Further mechanistic investigations demonstrated that SNHG18 inhibited c-Myc expression by modulating the ubiquitination-proteasome pathway and that c-Myc is the critical transcription factor that mediates SNHG18 inhibition of BC growth by directly binding to the p21 promoter, which was attributed with significant p21 accumulation. Conclusions SNHG18 promotes the transcription and expression of p21 by inhibiting c-Myc expression, leading to G0-G1 arrest and inhibiting the proliferation of bladder cancer cells. These findings highlight a novel cell cycle regulatory mechanism involving the SNHG18/c-Myc/p21 pathway in BC pathogenesis and could potentially lead to new lncRNA-based diagnostics and/or therapeutics for BC.
Predicting incidence density of COVID-19 rebound using tree-based machine learning algorithms
Authors: Liang Zhuoshuai , Tian Yuyang , Wang Yujian , Zhao Jiahui , Guo Ruifang , Qiu Xinwei , Lan Jia , Sun Xiaoyue , Liu Yawen , Liu Siyu 
Published: December 2022

Abstract
Introduction: A series of strategies adopted by the Chinese government can indeed control the COVID-19 epidemic, but they can also cause negative impact on people's mental health and economic incomes. How to balance the relationship between epidemic prevention and social development is an urgent topic for current research.
Methods: We included 122 rebound events involved 96 cities caused by Delta variant from May 21, 2021 to February 23, 2022 and corresponding 32 social environmental factors. Principal Component Analysis and K-Means were used for dimensionality reduction. Conventional logistic regression model, Random Forest model, and extreme Gradient Boosting model were used to model the factors for incidence density.
Results: A total of 96 cities were clustered into six categories. Cities with the number of cases or incidence density above the median are concentrated in cluster 1 and cluster 6. We selected “older”, “urbanratio”, “unemploy”, “serve”, and “air” as the optimal features, and constructed three concise models. The three models showed good discriminatory powers with AUCs of 0.666, 0.795, and 0.747.
Conclusion: Based on available public data, high prediction accuracy of the incidence density of COVID‐19 rebound can be achieved by machine learning methods. Developed level of cities may confer the rebound of COVID-19.
A New, Alternative Risk Score for Sarcopenia in Chinese Patients with Type 2 Diabetes Mellitus
Authors: He Qinghua , Wang Xiuzhi , Yang Caizhe , Zhuang Xiaoming , Yue Yanfen , Jing Hongjiang , Hu Jing , Sun Mingxiao , Guo Lixin 
Published: December 2022

Abstract
Objective: To develop a new, alternative sarcopenia risk score to screen for sarcopenia in type 2 diabetes patients in China and to demonstrate its validity.
Research design and methods: The data for this study came from a multicenter, cross-sectional study that had been designed to estimate the prevalence of sarcopenia among adults with type 2 diabetes and had been conducted in several hospitals in Beijing, China. A total of 1125 participants were randomly divided into two groups: an exploratory population and a validation population. A multivariable logistic regression model using the backward stepwise likelihood ratio method to estimate the probability of sarcopenia was fitted with candidate variables in the exploratory population. A new, alternative sarcopenia risk score was developed based on the multivariable model. The internal and external validations were performed in the exploratory and validation populations. The study was registered at Chinese Clinical Trial Registry (ChiCTR-EOC-15006901).
Results: The new, alternative sarcopenia risk score included seven variables: age, sex, BMI, glycosylated hemoglobin, insulin treatment or not, total caloric intake per day, and the proportion of calorie supplied by protein. The score ranged from -9 to 23. The area under the receiver operating characteristic (ROC) curve of the risk score for the prediction of sarcopenia in type 2 diabetes patients was 0.812 (95% CI 0.748–0.877) and 0.841 (95% CI 0.785–0.897) in the exploratory and validation populations, respectively. At the optimal cutoff value of 11, the sensitivity and specificity of the score for the prediction of sarcopenia were 74.5% and 78.0% in the exploratory population and 80.5% and 72.2% in the validation population, respectively. The Hosmer-Lemeshow goodness-of-fit test showed a good calibration with the risk score in external validation (χ2=1.687, P=0.989).
Conclusions: The new, alternative sarcopenia risk score appears to be an effective screening tool for the identification of sarcopenia in Chinese patients with type 2 diabetes in clinical practice.
Contextual barriers in effective interpersonal communication with patients: A qualitative analysis
Authors: Singhal Rameshwari , Dean Anil Chandra , Dubey Samiksha , Rastogi Pavitra , Lal Nand 
Published: December 2022

Abstract
Purpose-
Competence building in interpersonal communication between doctor and patients requires a deep understanding of the factors influencing their interaction. These factors are contextual and specific for individual clinical set-ups. The study explored these contextual factors from multi-source perspectives and analyzed the perceived barriers to interpersonal communication for formulating a training module for undergraduate doctor trainees/students.
Method-
Qualitative analysis using hermeneutic phenomenology was done from June 2018– February 2019 for patient’s semi-structured interviews (n=16); interns (n=15) and faculty (n=14) unstructured focus group. Three independent authors transcribed the data, derived codes and themes individually, and then collated and reflected on them to draw conclusions. Different collection methods, multisource perspectives, and multilevel analysis provided for triangulation and validation of the study results.
Results-
Seven inter-dependent barriers to interpersonal communication between students and patients were identified from this study’s multi-source feedback. They were time constraints, lack of trust and respect, cultural diversity, loco-regional linguistic differences, lack of empathy, illiteracy, and financial constraints. The authors felt that addressing these factors in training modules of communication skills would prepare undergraduate students towards patient-centered care and partnership.
Conclusions-
Within the limitations of this study, it can be concluded that every clinical microsystem has a unique set of barriers preventing interpersonal communication. Before preparing teaching modules for undergraduate students, it was necessary to explore these contextual barriers and address them towards the creation of a graduate competent in patient-centered interpersonal communication.
The improvement and mechanism of Scutellaria baicalensis Georgi stems and leaves flavonoids on okadaic acid-induced learning and memory impairment in rats
Authors: Shuai Ma , Congcong Xu , Yongcai Dong , Caixia Li , Shang Yazhen 
Published: December 2022

Abstract
Background
AD is a progressive neurodegenerative disease that seriously threatens the physical and mental health for the elderly. The disease is pathologically associated with Aβ plaque deposition, tau hyperphosphorylation, cholinergic damage, oxidative stress and neuroinflammation. okadaic acid (OA), an inhibitor of protein phosphatases PP1 and PP2A, can reduce the dephosphorylation of phosphorylated tau protein and result in the hyperphosphorylation of tau protein, which in turn leads to NFT deposition and indirectly induces AD. Therefore, the brain injury in mimic AD model established by intracerebroventricular injection of OA conforms to the pathogenesis of AD in rats.
Objective
To investigate the effect and mechanism of Scutellaria baicalensis Georgi stems and leaves flavonoids (SSFs) on learning and memory impairment induced by intracerebroventricular injection of OA in rats.
Methods
The mimic AD model of memory impairment of rats was established by intracerebroventricular injection of OA. Morris water maze was to screen the successful model of memory impairment. The successful model rats were randomly divided into model group, three doses of groups for SSFs 25, 50, and 100 mg/kg, and positive drug Ginkgo biloba tablet (GLT) 200 mg/kg group. The rats’ learning and memory abilities were detected with the Morris water maze. The morphological changes of nerve cells were observed by hematoxylin-eosin (HE) staining. The Choline acetyltransferase (ChAT) protein expressions were detected with immunohistochemistry (IHC). The phosphorylation level of tau protein at Ser262 and Ser396 sites was assayed with Western blotting (WB). The activities of inducible nitric oxide synthase (iNOS) and constitutive nitric oxide synthase (cNOS) were measured with ultraviolet spectrophotometry. Enzyme-linked immunosorbent assay (ELISA) was used to detect the level of inflammatory factors interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6).
Results
The intracerebroventricular injection of OA caused the rats’ learning and memory impairment and neuropathological changes. The protein expression of ChAT was reduced (P < 0.01) and the phosphorylation levels of tau protein at Ser262 and Ser396 were significantly increased (P < 0.01), the activity of iNOS increased (P < 0.01) and cNOS decreased (P < 0.05) in the hippocampus and cerebral cortex. The levels of IL-1β and TNF-α in the cerebral cortex were increased (P < 0.01), and the level of IL-6 was decreased (P < 0.05). The three doses of SSFs and GLT treatment to rats can differently ameliorate the above abnormal changes caused by OA.
Conclusion
SSFs can improve the rats’ learning and memory impairment caused by intracerebroventricular injection of OA and the effective mechanism may be related to the positive regulation in ChAT protein expression, tau hyperphosphorylation, NOS activity, and inflammatory cytokine levels.
Assessing the effect of sample storage time on viral detection using a rapid and cost-effective CTAB-based extraction method
Authors: Mark Deogratius , Sikazwe Geofrey , Saggaf Maliha , Tairo Fred , Ndunguru Joseph , Chiunga Evangelista , Lusana James , Kweka Elisiana , Maghembe Reuben , Bachwenkizi Hilda 
Published: December 2022

Abstract

Background: Cassava leaf samples can degrade quickly during storage and transportation from distant areas. Proper sampling and the use of efficient cheap storage methods are critical to obtain sufficient quality of DNA and RNA for plant virus epidemiology and to improve understanding on disease control. This is practical when samples are collected from isolated zones distant from a laboratory, or in developing countries that lack a supply of money and materials for virus diagnostics.
Results: The effect of sample storage duration on nucleic acid (NA) quality on virus detection was investigated in this study. A simple, rapid, and cost-effective CTAB-based approach (M3) for single NA extraction was optimized and evaluated together with two existing CTAB-based methods (M1 and M2) for extraction of NA from fresh and herbarium cassava leaves stored at; 1, 8, 26, and 56 months. The quantity of DNA and quality of both DNA and RNA were evaluated using Nanodrop 2000c UV–vis Spectrophotometer and agarose gel electrophoreses, and the rate of sample degradation was estimated using a simple mathematical model in Matlab computational software.
The results show that there is no significant difference between M1 and M2 in the mean concentration of DNA but there was a significant difference between M3 and the other two methods at p < 0.005. The mean concentration of DNA extracted using M3 was higher at 1 and 8 months of age. M3 and M2 produced high concentrations at the age of 26 and 56 months. Using a developed scale for quality score, M3 and M2 produced high-quality DNA from fresh samples. All methods produced poor-quality DNA and RNA at the age of 8 and 26 months and no visual bands at the age of 56 months. Statistically, there was a significant difference in the mean quality of DNA between M1 and M2 but there was no significant difference between M3 and the other two methods at p < 0.005. However, Cassava brown streak virus (CBSV) and Ugandan cassava brown streak virus (UCBSV) were readily detected by RT-PCR from RNA isolated using M3. The quality of DNA declined per storage time at the rate of 0.0493 and 0.0521/month while RNA was 0.0678 and 0.0744/month. Modified CTAB extracted a sufficient amount of NA of high quality for a third of the time (28/95min) compared to the existing two methods.
Conclusion: Our method will provide the cost-effective, quick, and simple processing of fresh and dry samples which will quicken and guide the decision process on when and what type of sample to process for plant disease management and surveillance actions.
Refined Instantaneous Frequency Estimation with Application to Sparse Signal Reconstruction
Authors: Ali Sadiq , Khan Nabeel Ali , Shah Feroz 
Published: December 2022

Abstract
In this study, we propose an instantaneous frequency (IF) estimation and then apply this algorithm for the reconstruction of missing samples. The proposed IF estimation and sparse reconstruction algorithm is developed by adding a refinement step where the crude estimates of IFs and signal components are further improved through the re-estimation stage. During the re-estimation stage, both IFs, as well as the signal components, are re-estimated by removing all the remaining components. Experimental results indicate that the proposed strategy improves both the accuracy of IF estimates as well as missing samples.