Candidate Indicators Research Articles

Development of an indicator of smoking status for people with multiple sclerosis in administrative data.

BackgroundAdministrative data lack health behavior information.MethodsWe developed an administrative case definition for past or current (‘ever smoking’) in 1320 individuals with MS from Manitoba, Canada. Candidate indicators for ‘ever smoked’ included smoking cessation medications, and diagnosis codes for tobacco use and chronic obstructive pulmonary disease, using variable lookback periods.ResultsWhen compared to self-reported smoking status, the case definition incorporating all indicators over a lifetime lookback period had a sensitivity of 31.98%, and positive predictive value of 78.26%.ConclusionThis smoking status definition could only partially control for confounding due to smoking because of the low sensitivity.

基于地表土壤动物与植物完整性指数评估若尔盖沼泽湿地受扰现状

利用地表土壤动物与植物群落生物完整性指数评价若尔盖沼泽湿地受扰现状，为若尔盖沼泽湿地恢复提供依据。2018年7月与2019年7月对若尔盖10处典型湿地（参照区4个，受扰区6个）地表土壤动物与湿地植被群落进行调查。通过对74个候选指标的分布范围、判别能力及相关分析，确定中生性植物、一年生植物、龙胆科植物、一年生植物/多年生植物比4个植物核心指标，地表土壤动物总个体数量、中小型动物类群数量、蜘蛛目物种数量、菌食性土壤动物与腐食性土壤动物5个核心指标，构建若尔盖沼泽湿地地表土壤动物与植物完整性指数。以所有采样点95%分位数为最佳期望值，四分位法确定研究区域未受干扰、轻度干扰程度、中度干扰与重度干扰4个等级，作为判断若尔盖沼泽湿地受扰状况。结果显示，若尔盖沼泽湿地相对原始沼泽、花湖沼泽化草甸2、长期低强度排水疏干区、短期高强度排水疏干区分别处于未受干扰、轻度干扰、中度干扰与重度干扰状态。所调查的若尔盖典型湿地中，20%的湿地未受到干扰，30%的湿地受到轻度干扰，30%的湿地受到中度干扰，20%的湿地受到重度干扰。Pearson相关系数分析显示，地表土壤动物完整性指数与植物完整性指数存在显著正相关关系（r=0.722，P<0.05），表明地表土壤动物完整性指数与植物完整性指数评价结果具有一致性。;The Zoige wetland, located at the northeast of Qinghai-Tibet Plateau, is the largest plateau wetland in China. There have been substantial changes in Zoige wetland due to natural (climate, soil, and vegetation) and anthropogenic (drainage and overgrazing) impacts. The index of biotic integrity is a comprehensive method for assessing the ecological status of wetlands. The ground-dwelling soil animal and wetland plant are the most widely used biological indicators owing to their distinct advantages for wetland ecosystem monitoring, while there is no indicator framework to determine the disturbed status and benefit of returning grazing to wetland of the Zoige area. The aim of this study is to evaluate disturbance status of Zoige wetland using the ground-dwelling soil animal and wetland plant-based index of biotic integrity to providing data for the restoration of Zoige wetland. In July 2018 and July 2019, the ground dwelling soil animal and plant were investigated from 10 typical wetlands of Zoige were chosen, including 4 wetlands which were undisturbed or less disturbed, and 6 wetlands which were disturbed. The ground-dwelling soil animal and plant-based index of biotic integrity in Zoige wetland were determined through a range of index value distribution, discrimination ability, and correlation analysis of 74 candidate indicators, which included 5 metrics of the number of soil animal individuals, the number of meso/micro soil animal individual, the number of ground spiders, Fungivorous form animal and Saprozoic animal, while plant integrity index including 4 metrics of mesophyte, annuals plant, Gentians, ration of annuals/Perennials. Use the quartile method to determine four grades of undisturbed, mild, moderate, severe disturbance in the study area with 95% quantile of all sampling points as the best-expected value and judge the disturbance status of the Zoige swamp wetland. The results showed that the relatively primary wetland was undisturbance, swamp meadow 2 was mild disturbance, long-term low intensity drainage stress area was moderate disturbance, and short-term high intensity drainage stress area was severe disturbance. Among the investigated typical wetland of Zoige, 20% of wetlands were undisturbance, 30% were mild disturbance, 30% were moderate disturbance, and 20% were the severe disturbance. According to the Pearson correlation coefficient analysis, there was a significantly positive correlation between surface soil animal integrity index and plant communities integrity index (r=0.722, P<0.05), indicating that the evaluation results of surface soil animal integrity index and plant integrity index were consistent.

Political Attacks in 280 Characters or Less: A New Tool for the Automated Classification of Campaign Negativity on Social Media

Negativity in election campaign matters. To what extent can the content of social media posts provide a reliable indicator of candidates' campaign negativity? We introduce and critically assess an automated classification procedure that we trained to annotate more than 16,000 tweets of candidates competing in the 2018 Senate Midterms. The algorithm is able to identify the presence of political attacks (both in general, and specifically for character and policy attacks) and incivility. Due to the novel nature of the instrument, the article discusses the external and convergent validity of these measures. Results suggest that automated classifications are able to provide reliable measurements of campaign negativity. Triangulations with independent data show that our automatic classification is strongly associated with the experts’ perceptions of the candidates’ campaign. Furthermore, variations in our measures of negativity can be explained by theoretically relevant factors at the candidate and context levels (e.g., incumbency status and candidate gender); theoretically meaningful trends are also found when replicating the analysis using tweets for the 2020 Senate election, coded using the automated classifier developed for 2018. The implications of such results for the automated coding of campaign negativity in social media are discussed.

Design of Breakdown and Checklist for Continuous Renal Replacement Therapy

Objective. This study aimed to improve the quality of continuous renal replacement therapy (CRRT). Methods. A pool of candidate indicators was established using literature retrieval, panel discussion, and experience summary. The first round of consultation was performed with the selected 18 experts by the Delphi method. Then, the checklist was modified according to the experts’ opinions for the second round of consultation to prepare the final checklist. Results. The positivity coefficients of experts in the two rounds of consultation were 100% and 88.9%, respectively, with the authority coefficient of 0.88. The Kendall coordination coefficients of the primary and secondary indicators were 0.296 and 0.303, respectively ( P &lt; 0.05 ). Finally, the breakdown and checklist were prepared, which involved 16 primary indicators and 56 secondary indicators. Conclusion. The scientific and reasonable breakdown and checklist prepared based on a consultation can provide scientific guidance for nursing during CRRT, reduce the incidence of adverse events, and improve work efficiency and satisfaction of medical care.

Towards a Positive Welfare Protocol for Cattle: A Critical Review of Indicators and Suggestion of How We Might Proceed

Current animal welfare protocols focus on demonstrating the absence (or at least low levels) of indicators of poor welfare, potentially creating a mismatch between what is expected by society (an assurance of good animal welfare) and what is actually being delivered (an assurance of the absence of welfare problems). This paper explores how far we have come, and what work still needs to be done, if we are to develop a protocol for use on commercial dairy farms where the aim is to demonstrate the presence of positive welfare. Following conceptual considerations around a perceived “ideal” protocol, we propose that a future protocol should be constructed (i) of animal-based measures, (ii) of indicators of affective state, and (iii) be structured according to indicators of short-term emotion, medium-term moods and long-term cumulative assessment of negative and positive experiences of an animal's life until now (in contrast to the current focus on indicators that represent different domains/criteria of welfare). These three conditions imposed the overall structure within which we selected our indicators. The paper includes a critical review of the literature on potential indicators of positive affective states in cattle. Based on evidence about the validity and reliability of the different indicators, we select ear position, play, allogrooming, brush use and QBA as candidate indicators that we suggest could form a prototype positive welfare protocol. We emphasise that this prototype protocol has not been tested in practice and so it is perhaps not the protocol itself that is the main outcome of this paper, but the process of trying to develop it. In a final section of this paper, we reflect on some of the lessons learnt from this exercise and speculate on future perspectives. For example, while we consider we have moved towards a prototype positive welfare protocol for short-term affective states, future research energy should be directed towards valid indicators for the medium and long-term.

Development of Policy-Relevant Indicators for Injury Prevention in British Columbia by the Key Decision-Makers.

Indicators can help decision-makers evaluate interventions in a complex, multi-sectoral injury system. We aimed to create indicators for road safety, seniors falls, and ‘all-injuries’ to inform and evaluate injury prevention initiatives in British Columbia, Canada. The indicator development process involved a five-stage mixed methodology approach, including an environmental scan of existing indicators, generating expert consensus, selection of decision-makers and conducting a survey, selection of final indicators, and specification of indicators. An Indicator Reference Group (IRG) reviewed the list of indicators retrieved in the environmental scan and selected candidate indicators through expert consensus based on importance, modifiability, acceptance, and practicality. Key decision-makers (n = 561) were invited to rank each indicator in terms of importance and actionability (online survey). The IRG applied inclusion criteria and thresholds to survey responses from decision-makers, which resulted in the selection of 47 road safety, 18 seniors falls, and 33 all-injury indicators. After grouping “like” indicators, a final list of 23 road safety, 8 seniors falls, and 13 all-injury indicators were specified. By considering both decision-maker ranking and expert opinion, we anticipate improved injury system performance through advocacy, accountability, and evidence-based resource allocation in priority areas. Our indicators will inform a data management framework for whole-system reporting to drive policy and funding for provincial injury prevention improvement.

Development and Internal Validation of a Disability Algorithm for Multiple Sclerosis in Administrative Data

Objective: We developed and internally validated an algorithm for disability status in multiple sclerosis (MS) using administrative data.Methods: We linked administrative data from Manitoba, Canada to a clinical dataset with Expanded Disability Status Scale (EDSS) scores for people with MS. Clinical EDSS scores constituted the reference standard. We created candidate indicators using the administrative data. These included indicators based on use of particular health care services (home care, long-term care, rehabilitation admission), use of specific diagnostic codes (such as spasticity, quadriplegia), and codes based on use of Employment and Income Insurance. We developed algorithms to predict severe disability (EDSS ≥6.0), and to predict disability as a continuous measure. We manually developed algorithms, and also employed regression approaches. After we selected our preferred algorithms for disability, we tested their association with health care use due to any cause and infection after potential confounders.Results: We linked clinical and administrative data for 1,767 persons with MS, most of whom were women living in urban areas. All individual indicators tested had specificities >90% for severe disability, and all but a diagnosis of visual disturbance had positive predictive values (PPV) >70%. The combination of home care or long-term care use or rehabilitation admission had a sensitivity of 61.9%, specificity of 90.76%, PPV of 70.06% and negative predictive of 87.21%. Based on regression modeling, the best-performing algorithm for predicting the EDSS as a continuous variable included age, home care use, long-term care admission, admission for rehabilitation, visual disturbance, other paralytic syndromes and spasticity. The mean difference between observed and predicted values of the EDSS was −0.0644 (95%CI −0.1632, 0.0304). Greater disability, whether measured using the clinical EDSS or either of the administrative data algorithms was similarly associated with increased hospitalization rates due to any cause and infection.Conclusion: We developed and internally validated an algorithm for disability in MS using administrative data that may support population-based studies that wish to account for disability status but do not have access to clinical data sources with this information. We also found that more severe disability is associated with increased health care use, including due to infection.

Current trends in candidate selection, contraindications, and indications for lung transplantation.

Lung transplantation is an established treatment option that can improve quality of life and prolong survival for select patients diagnosed with end-stage lung disease. Given the gaps in organ donation and failures to make effective use of available organs, careful selection of candidates for lung transplant remains one of the most important considerations of the transplant community. Toward this end, we briefly reviewed recent trends in pretransplant evaluation, candidate selection, organ allocation, and organ preservation techniques. Since the latest consensus statement regarding appropriate selection of lung transplant candidates, many advances in the science and practice of lung transplantation have emerged and influenced our perspective of ‘contraindications’ to transplant. These advances have made it increasingly possible to pursue lung transplant in patients with risk factors for decreased survival—namely, older recipient age, increased body mass index, previous chest surgery, poorer nutritional status, and presence of chronic infection, cardiovascular disease, or extrapulmonary comorbid conditions. Therefore, we reviewed the updated evidence demonstrating the prognostic impact of these risk factors in lung transplant recipients. Lastly, we reviewed the salient evidence for current trends in disease-specific indications for lung transplantation, such as chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, cystic fibrosis, emphysema due to alpha-1 antitrypsin deficiency, and pulmonary arterial hypertension, among other less common end-stage diseases. Overall, lung transplant remains an exciting field with considerable hope for patients as they experience remarkable improvements in quality of life and survival in the modern era.

Ultrasound Echogenicity-based Assessment of Muscle Fatigue During Functional Electrical Stimulation.

The rapid onset of muscle fatigue during functional electrical stimulation (FES) is a major challenge when attempting to perform long-term periodic tasks such as walking. Surface electromyography (sEMG) is frequently used to detect muscle fatigue for both volitional and FES-evoked muscle contraction. However, sEMG contamination from both FES stimulation artifacts and residual M-wave signals requires sophisticated processing to get clean signals and evaluate the muscle fatigue level. The objective of this paper is to investigate the feasibility of computationally efficient ultrasound (US) echogenicity as a candidate indicator of FES-induced muscle fatigue. We conducted isometric and dynamic ankle dorsiflexion experiments with electrically stimulated tibialis anterior (TA) muscle on three human participants. During a fatigue protocol, we synchronously recorded isometric dorsiflexion force, dynamic dorsiflexion angle, US images, and stimulation intensity. The temporal US echogenicity from US images was calculated based on a gray-scaled analysis to assess the decrease in dorsiflexion force or motion range due to FES-induced TA muscle fatigue. The results showed a monotonic reduction in US echogenicity change along with the fatigue progression for both isometric (R2 =0.870±0.026) and dynamic (R2 =0.803±0.048) ankle dorsiflexion. These results implied a strong linear relationship between US echogenicity and TA muscle fatigue level. The findings indicate that US echogenicity may be a promising computationally efficient indicator for assessing FES-induced muscle fatigue and may aid in the design of muscle-in-the-loop FES controllers that consider the onset of muscle fatigue.

Beware of pharyngeal Fusobacterium nucleatum in COVID-19

BackgroundFusobacterium nucleatum (F. n) is an important opportunistic pathogen causing oral and gastrointestinal disease. Faecalibacterium prausnitzii (F. p) is a next-generation probiotic and could serve as a biomarker of gut eubiosis/dysbiosis to some extent. Alterations in the human oral and gut microbiomes are associated with viral respiratory infection. The aim of this study was to characterise the oral and fecal bacterial biomarker (i.e., F. n and F. p) in COVID-19 patients by qPCR and investigate the pharyngeal microbiome of COVID-19 patients through metagenomic next-generation sequencing (mNGS).ResultsPharyngeal F. n was significantly increased in COVID-19 patients, and it was higher in male than female patients. Increased abundance of pharyngeal F. n was associated with a higher risk of a positive SARS-CoV-2 test (adjusted OR = 1.32, 95% CI = 1.06 ~ 1.65, P < 0.05). A classifier to distinguish COVID-19 patients from the healthy controls based on the pharyngeal F. n was constructed and achieved an area under the curve (AUC) of 0.843 (95% CI = 0.688 ~ 0.940, P < 0.001). However, the level of fecal F. n and fecal F. p remained unaltered between groups. Besides, mNGS showed that the pharyngeal swabs of COVID-19 patients were dominated by opportunistic pathogens.ConclusionsPharyngeal but not fecal F. n was significantly increased in COVID-19 patients, clinicians should pay careful attention to potential coinfection. Pharyngeal F. n may serve as a promising candidate indicator for COVID-19.

The prioritization and development of key social and structural indicators to address gaps in a framework for monitoring the Strategies toward Ending Preventable Maternal Mortality (EPMM): Results of an iterative expert technical consultation.

BackgroundSince 2014, iterative technical work has captured stakeholder demand and channeled it toward improving maternal health measurement, to support SDG 3.1. Strategies toward Ending Preventable Maternal Mortality (EPMM) (2015) turned a broad lens on upstream systemic determinants of maternal health and survival highlighted in 11 Key Themes. A monitoring framework was developed to help countries track progress across these domains. This process yielded requests for additional indicators where stakeholders identified gaps for tracking EPMM Key Themes. In response, two technical consultations aimed at affirming the measurement gaps, specifying the constructs for measurement, and fully elaborating the metadata to allow them to be monitored.MethodsMeasures for development were prioritized based on multi-stakeholder dialogues in five countries, and data collected from government officials and UN partners in twenty countries on perceived need for proposed additional indicators. Sixty-one participants representing expertise in measure development and the topical areas covered took part across both consultations. Measures were developed through two simultaneous participatory online consultations stratified by focus area, comprising videos, discussion forums, polls, and live Zoom meetings.ResultsEight candidate indicators relevant to priority recommendations in the EPMM Strategies are presented. Each includes a definition, numerator and denominator (if applicable), method of estimation, disaggregation factors, preferred data source(s), and expected periodicity. Four address gaps in measures of fundamental rights-related determinants of maternal health at national and subnational level, including women’s reproductive autonomy; participative accountability for maternal health outcomes; and Respectful Maternity Care. Four strengthen the ability to count, track, and link births and maternal deaths and causes of death.ConclusionsThe proposed indicators correspond to specific EPMM Key Themes, filling gaps identified by multiple stakeholders, and respond to calls for a broadened approach to measurement and for indicators that track the social and health-systems determinants of maternal health. They reflect inputs and aspirations of numerous stakeholders, gathered over time and across various platforms. The iterative, discursive exploration of the concepts for measurement and the need for metrics to track them responds to recent calls for measure development to be carried out in more inclusive ways, and to be primarily concept- and user-driven.

Searching for Indicator Species of High Floristic Quality Depressional Wetlands in the US Southern Plains

Floristic Quality Assessment requires compiling a full list of vascular plant species for the wetland. Practitioners may lack the time and taxonomic skills for full-community vegetation surveys, especially when wetlands are large and complex. In this paper we broadly ask whether floristic quality indicator species may exist for wetlands, specifically evaluating indicator species potential for high floristic quality depressional wetlands in the US southern plains. Candidate indicators were identified for a broader context (depressions across Oklahoma prairie ecoregions) and narrower context (depressions in the northern Central Great Plains of Oklahoma) and evaluated based on performance, validity, and robustness criteria. Nine individual species and two species pairs showed exclusivity and ubiquity for high floristic quality, with their value generally improving in the narrower context. However, the overall best indicator (Eleocharis compressa) frequently occurred (> 20 % rate) in lower quality validation sites, and all indicators were lacking in one or more criteria. Combining E. compressa with select other candidates (Ammannia coccinea, Juncus torreyi, Leersia oryzoides) may compensate for weaknesses of individual species but the combinations may rarely be found across the region, suggesting they may not be useful in practice or that high-quality conditions are in fact scarce. Overall, these results offer mixed support for relying on indicator species to rapidly identify or verify high floristic quality depressional wetlands in the US southern plains. We recommend similar studies with larger datasets in other regions and testing other quality levels (low, moderate) before broadly concluding whether floristic quality indicator species may exist for wetlands.

Tipping Cascades in a Multi-patch System with Noise and Spatial Coupling

Forecasting tipping points in spatially extended systems is a key area of interest to ecologists. A slowly declining spatially distributed population is an important example of an ecological system that could exhibit a cascade of tipping points. Here, we develop a spatial two-patch model with environmental stochasticity that is slowly forced through population collapse, in the presence of changing environmental conditions. We begin with a basic spatial model, then introduce a fast–slow version of the model using geometric singular perturbation theory, followed by the inclusion of stochasticity. Using the spectral density of the fluctuating subpopulation in each patch, we derive analytic expressions for candidate indicators of population extinction and evaluate their performance through a simulation study. We find that coupling and spatial heterogeneity decrease the magnitude of the proposed indicators in coupled populations relative to isolated populations. Moreover, the degree of coupling dictates the trends in summary statistics. We conclude that this theory may be applied to other contexts, including the control of invasive species.

Influence of social and economic development on water quality in Dongting Lake

Lakes have faced the challenge of alleviating water deterioration due to rapid socioeconomic development. This fact highlights the urgency to understand the impact of socioeconomic development on water quality. Although existing studies have devoted efforts on the research of the relationship between socioeconomic development and water quality, the theoretical basis and process of factors selection (socioeconomic factors and water quality factors) have often been ignored. A conceptual model was employed in this work to select the candidate indicators. Meanwhile, a new developed method that introduces a monthly correction coefficient was proposed to evaluate socioeconomic indicators and seasonal-trend decomposition (STL) using the locally weighted scatterplot smoothing (LOESS) method. At the same time, the steady-state transformation index (RSI) were adopted to further select water quality indicators. Base on that, canonical correlation analysis (CCA) and the measured and ideal values of water quality indicators were used to quantitatively study the degree of influence of socioeconomic systems and their key factors on water quality. The proposed methodology were applied to the Dongting Lake basin and its four sub-watersheds, the Xiang, Zi, Yuan and Li River watersheds, as these water bodies have been seriously affected by human activities in recent years. The results showed that there was a high correlation between the socioeconomic system and water quality, with significant temporal and spatial characteristics. The average annual impact degree of the socioeconomic system on water quality was at a moderate level for Dongting Lake basin and its four sub-watersheds, while the impact degree for Xiang River watershed was higher than that of the other watersheds. The influence degree decreased in the Dongting Lake basin, Xiang River and Li River watersheds, whereas the trend was the opposite in the Yuan River and Zi River watersheds from 2004 to 2017. Water consumption (WC), percentage of cultivated land area (CA), total nitrogen (TN) input from agricultural non-point sources (A_TN), and human response factors, such as industrial wastewater compliance discharge rate (WR) and sewage treatment (ST), were the main socioeconomic factors affecting the water quality of Dongting Lake. Implementation of an efficient water use plan, controlling the agricultural non-point source pollution and improving the sewage treatment capacity should be given priority in order to effectively improve the water quality of Dongting Lake. The present study showed the relationship between socioeconomic drivers and water quality, and provided a reliable tool for decision makers in watershed management.

Patient, Family, and Clinician Perspectives on End-of-Life Care Quality Domains and Candidate Indicators for Adolescents and Young Adults With Cancer

End-of-life care quality indicators specific to adolescents and young adults (AYAs) aged 12 to 39 years with cancer have not been developed. To identify priority domains for end-of-life care from the perspectives of AYAs, family caregivers, and clinicians, and to propose candidate quality indicators reflecting priorities. This qualitative study was conducted from December 6, 2018, to January 5, 2021, with no additional follow-up. In-depth interviews were conducted with patients, family caregivers, and clinicians and included a content analysis of resulting transcripts. A multidisciplinary advisory group translated priorities into proposed quality indicators. Interviews were conducted at the Dana-Farber Cancer Institute, Kaiser Permanente Northern California, Kaiser Permanente Southern California, and an AYA cancer support community (lacunaloft.org). Participants included 23 AYAs, 28 caregivers, and 29 clinicians. Stage IV or recurrent cancer. Care priorities. Interviews were conducted with 23 patients (mean [SD] age, 29.3 [7.3] years; 12 men [52%]; 18 White participants [78%]), 28 family caregivers (23 women [82%]; 14 White participants [50%]), and 29 clinicians (20 women [69%]; 13 White participants [45%]). Caregivers included 22 parents (79%), 5 spouses or partners (18%), and 1 other family member (4%); the 29 clinicians included 15 physicians (52%), 6 nurses or nurse practitioners (21%), and 8 social workers or psychologists (28%). Interviews identified 7 end-of-life priority domains: attention to physical symptoms, attention to quality of life, psychosocial and spiritual care, communication and decision-making, relationships with clinicians, care and treatment, and independence. Themes were consistent across the AYA age range and participant type. Although some domains were represented in quality indicators developed for adults, unique domains were identified, as well as AYA-specific manifestations of existing domains. For example, quality of life included global quality of life; attainment of life goals, legacy, and meaning; support of personal relationships; and normalcy. Within communication and decision-making, domains included communication early in the disease course, addressing prognosis and what to expect at the end of life, and opportunity for AYAs to hold desired roles in decision-making. Care and treatment domains relevant to cancer therapy, use of life-prolonging measures, and location of death emphasized the need for preference sensitivity rather than a standard path. This finding differs from existing adult indicators that propose that late-life chemotherapy, intensive measures, and hospital death should be rare. The findings of this qualitative study suggest that AYAs with cancer have priorities for care at the end of life that are not fully encompassed in existing indicators for adults. Use of new indicators for this young population may better reflect patient- and family-centered experiences of quality care.

DNA methylations of brain-derived neurotrophic factor exon VI are associated with major depressive disorder and antidepressant-induced remission in females

BackgroundBrain-derived neurotrophic factor (BDNF) has been suggested to play important roles in major depressive disorder (MDD) and antidepressant treatment. The main purpose of this study was to evaluate the association of DNA methylation changes in the BDNF gene with MDD and antidepressant treatment. MethodsA total of 291 MDD patients and 100 healthy controls were included and followed up for 6 weeks. The Hamilton Depression Rating Scale-17 (HDRS-17) was used to measure treatment improvement. The life events scales (LES) and childhood trauma questionnaire (CTQ) were used to rate recent and early life stress. DNA methylation levels of CpG sites in the BDNF gene were measured. ResultsTwo CpG sites in BDNF exon VI (BDNF133 and BDNF134) were demonstrated to have significantly higher methylation in MDD patients than in controls (both FDR-adjusted P = 0.001). A logistics regression model indicated that the interaction between the hypermethylation of BDNF133 and negative subscore of LES was associated to MDD (OR=0.0075, P<0.001). Methylation of BDNF140 at baseline was significantly elevated in remitters (FDR-adjusted P = 0.046) at week 6. In subgroup analyses, these findings could be replicated in females, but not in males. LimitationsThe methylation status of BDNF after 6 weeks of antidepressant treatment was not measured and the DNA methylation were detected in peripheral blood cells. ConclusionsThese findings highlight gender-specific alteration of methylation at several CpG sites in BDNF exon VI as a promising candidate indicator of MDD and antidepressant-induced remission.

Albumin concentration is a candidate indicator for predicting the risk of calf venous thromboembolism in patients undergoing gynecologic surgery.

To explore the ability of albumin concentration to predict calf venous thromboembolism (cVTE) in gynecologic diseases. We analyzed data from 761 patients from the gynecology department. We screened the serum albumin concentration as an important indicator for predicting cVTE through logistic analysis. The data were divided into albumin below 35 and 35g/L or more. Receiver operating characteristics analysis was used to compare the predictive ability of albumin, D-dimer, and a combination of these parameters as indicators for cVTE risk in different groups and subgroups. In gynecologic diseases, the albumin concentrations were lower in the surgery and malignancy group than in the chemotherapy and benign disease group. Albumin concentration had a predictive ability for cVTE risk. In ovarian cancer patients with albumin concentrations less than 35g/L, albumin was better than D-dimer at predicting cVTE (area under the curve [AUC] 0.79, 95% confidence interval [CI] 0.70-0.87, P<0.001 versus AUC 0.65, 95% CI 0.54-0.77, P=0.016). The albumin concentration was a candidate indicator for predicting cVTE in surgical patients in the gynecology department, especially in ovarian cancer patients with albumin concentrations less than 35g/L. A combination of the albumin and D-dimer parameters may improve the predictive ability for cVTE.

High Expression Levels of CDK1 and CDC20 in Patients With Lung Squamous Cell Carcinoma are Associated With Worse Prognosis.

Purpose: Progress related to the early detection and molecular targeted therapy of lung squamous cell carcinoma (LUSC) remains limited. The goal of our study was to identify key candidate indicators of LUSC. Methods: Three microarray datasets (GSE33532, GSE30219 and GSE19188) were applied to find differentially expressed genes (DEGs). Functional enrichment analyses of DEGs were carried out, and their protein-protein interaction (PPI) network was established. Hub genes were chosen from the PPI network according to their degree scores. Then, overall survival (OS) analyses of hub genes were carried out using Kaplan-Meier plotter, and their GSEA analyses were performed. Public databases were used to verify the expression patterns of CDK1 and CDC20. Furthermore, basic experiments were performed to verify our findings. Results: A total of 1,366 DEGs were identified, containing 669 downregulated and 697 upregulated DEGs. These DEGs were primarily enriched in cell cycle, chromosome centromeric region and nuclear division. Seventeen hub genes were selected from PPI network. Survival analyses demonstrated that CDK1 and CDC20 were closely associated with OS. GSEA analyses revealed that cell cycle, DNA replication, and mismatch repair were associated with CDK1 expression, while spliceosome, RNA degradation and cell cycle were correlated with CDC20 expression. Based on The Cancer Genome Atlas (TCGA) and The Human Protein Atlas (THPA) databases, CDK1 and CDC20 were upregulated in LUSC at the mRNA and protein levels. Moreover, basic experiments also supported the obvious upregulation of CDK1 and CDC20 in LUSC. Conclusion: Our study suggests and validates that CDK1 and CDC20 are potential therapeutic targets and prognostic biomarkers of LUSC.

Exploring Perinatal Indicators of Infant Social-Emotional Development: A Review of the Replicated Evidence.

The importance of infant social-emotional development for outcomes across the lifecourse has been amply demonstrated. Despite this, most screening measures of social-emotional development are designed for children 18months of age and over, with a clear gap in earlier infancy. Nosystematicreview has yet harvested the evidence for candidate indicators in the perinatal window. This paper examines modifiable risk and protective factors for two seminal earlymarkers of social-emotional development: attachment security and behavioral regulation mid-infancy. We searched meta-analytic and longitudinal studies of developmental relationships between modifiable exposures in the perinatal window (pregnancy to 10months postpartum) and attachment and behavioral regulation status measured between 12 and 18months. Six electronic databases were used: ERIC, PsycINFO, Medline Complete, Informit, Embase, and Scopus. Twelve meta-analytic reviews and 38 original studies found replicated evidence for 12 indicators across infant, caregiving, and contextual domains predictive of infant behavioral regulation and attachment status between 12 and 18months. Key among these were caregiving responsiveness, maternal mental health, couple relationship, and SES as a contextual factor. Perinatal factors most proximal to the infant had the strongest associations with social-emotional status. Beyond very low birthweight and medical risk, evidence for infant-specific factors was weaker. Risk and protective relationships were related but not always inverse. Findings from this review have the potential to inform the development of reliable tools for early screening of infant social-emotional development for application in primary care and population health contexts.

Big data driven Internet of Things for credit evaluation and early warning in finance

The big data technology framework has been successfully used in the Internet of Things, and the financial industry also hopes to use the advanced technology of big data to integrate and improve internal and external data related to credit risks. Relying on more efficient machine learning algorithms to get a reasonable prediction of credit risk can reduce the self-generated losses of the Internet of Things finance and increase profits. This article uses distributed search engine technology to customize web crawlers to obtain the required bank card and transaction data from the multi-source heterogeneous data of the Internet of Things financial industry, design the corresponding Spark parallel algorithm to preprocess the data, and establish an inverted table and two Level index file provides data source for big data analysis platform. After the data source is determined, the Mutually Exclusive Collectively Exhaustive (MECE) analysis method is combined with the scores of many financial business experts in the industry to obtain a set of candidate indicators and quantification methods for the financial credit risk evaluation of the Internet of Things, and analyze the correlation of indicators and risk grading. The random forest algorithm in the big data machine learning library is used to select the feature of the candidate index set, and a multi-level spatial association rule algorithm based on the Hash structure is designed to mine the financial risk information of the Internet of Things, and build a credit risk assessment and intelligent early warning model. This paper selects 26 indicators of Internet of Things finance as the research objects, uses SPSS26.0 software to perform sample Kaiser–Meyer–Olkin (KMO) test and Bartlett sphere test on the original data, and describes the results of factor analysis in detail. The particle swarm algorithm is introduced into the parameter optimization of random forest, and the financial credit risk assessment model of the Internet of Things is established. The results show that this method can significantly reduce the probability of banks making the first and second error rates when evaluating the credit risk of financing the Internet of Things Finance. This is conducive to the smooth development of the Internet of Things financial business for banks, which enables banks to enhance their own profitability while effectively reducing losses due to incorrect credit provision.