Assessment of the perceived burden associated with Malignant Melanoma with Pictorial Representation of Illness and Self Measure (PRISM) and Melanoma Concerns Questionnaire (MCQ-28)

Purpose The impact of malignant melanoma (MM) on patients’ psychophysical well-being has been poorly addressed. We aimed to assess the perceived burden in patients with a diagnosis of MM, using two different tools, one generic and one specific for MM, such as Pictorial Representation of Illness and Self Measure (PRISM) and Melanoma Concerns Questionnaire (MCQ-28), respectively. The correlation between PRISM and MCQ-28 subscales and the relevance of disease and patient-related variables were also investigated. Methods This single-centre, cross-sectional study included all adult consecutive MM patients who attended our Dermatology Unit from December 2020 to June 2021. Demographics and disease-related data were recorded. PRISM and MCQ-28 were administered. Results One hundred and seventy-one patients were included (mean age: 59.5 ±14.9 years.; 48.0% males). Median time from MM diagnosis to inclusion was 36 months. Nearly 80% of the patients had in situ or stage I MM. Overall, 22.2% of the patients reported a PRISM score <100mm and similar percentages provided scores indicating impaired quality of life, as assessed with MCQ-28 subscales. A weak, albeit significant, correlation was found between PRISM scores and ACP, CON and SOC2 subscales. The most relevant association found was that between lower PRISM scores and higher-stage MM. Conclusions In the study population, mostly affected with superficial MM, their perception of the burden associated with MM did not appear either particularly dramatic or disabling. PRISM seems a reliable system for capturing and quantifying the domains correlated with the emotive dimension of MM, especially MM-related concerns and willingness to face life

GPS-based fine-scale mapping surveys for schistosomiasis assessment: a practical introduction and documentation of field implementation

Background Fine-scale mapping of schistosomiasis to guide micro-targeting of interventions will gain importance in elimination settings, where the heterogeneity of transmission is often pronounced. Novel mobile applications offer new opportunities for disease mapping. We provide a practical introduction and documentation of the strengths and shortcomings of GPS-based household identification and participant recruitment using tablet-based applications for fine-scale schistosomiasis mapping at sub-district level in a remote area in Pemba, Tanzania. Methods A community-based household survey for urogenital schistosomiasis assessment was conducted from November 2020 until February 2021 in 20 small administrative areas in Pemba. For the survey, 1400 housing structures were prospectively and randomly selected from shapefile data. To identify pre-selected structures and collect survey-related data, field enumerators searched for the houses’ geolocation using the mobile applications Open Data Kit (ODK) and MAPS.ME. The number of inhabited and uninhabited structures, the median distance between the pre-selected and recorded locations, and the dropout rates due to non-participation or non-submission of urine samples of sufficient volume for schistosomiasis testing was assessed. Results Among the 1400 randomly selected housing structures, 1396 (99.7%) were identified by the enumerators. The median distance between the pre-selected and recorded structures was 5.4 m. A total of 1098 (78.7%) were residential houses. Among them, 99 (9.0%) were dropped due to continuous absence of residents and 40 (3.6%) households refused to participate. In 797 (83.1%) among the 959 participating households, all eligible household members or all but one provided a urine sample of sufficient volume. Conclusions The fine-scale mapping approach using a combination of ODK and an offline navigation application installed on tablet computers allows a very precise identification of housing structures. Dropouts due to non-residential housing structures, absence, non-participation and lack of urine need to be considered in survey designs. Our findings can guide the planning and implementation of future household-based mapping or longitudinal surveys and thus support micro-targeting and follow-up of interventions for schistosomiasis control and elimination in remote areas. Trial registration ISRCTN, ISCRCTN91431493. Registered 11 February 2020, https://www.isrctn.com/ISRCTN91431493

The Impacts of Market Segmentation on Thermal Power Generation Efficiency

China’s power industry is in a critical transformation period. The new round of power system reform in 2015 will have a profound impact on China’s power industry. Therefore, it’s necessary to analyze the influencing factors of thermal power generation efficiency. Based on the thermal power generation industry related data in China’s 30 provinces from 2005 to 2017, this paper studies the impacts of market segmentation on thermal power generation efficiency in China. And the empirical result shows that the market segmentation exhibit significant negative effects on the thermal power generation efficiency, that is, the thermal power generation efficiency significantly decrease 1.6799 for each unit increase of market segmentation index of thermal power industry. Besides, by decomposing the dynamic thermal power efficiency index, we find that the “innovation effect” is the primary channel for the market segmentation to make effects on the thermal power generation efficiency. Furthermore, our findings are still robust after considering endogenous problems and eliminating the relevant data. Finally, research conclusions of our study paper provide empirical supports for the efficient development of China’s power market.

What Really Matters for Returning to Work After Breast Cancer? A 6-month Longitudinal Study and a Multidimensional Approach

Purpose. Return to work (RTW) after breast cancer (BC) may easily impact on women recovery and quality of life. Literature on RTW hightlighed several factors associated to RTW after BC, and there is still some concern for exploring the main sociodemographic, clinical, psychological and work-related predictors of RTW after BC treatments especially when considering the first 6 months. The present study aims to explore which baseline factors are associated with RTW at 6-month after BC surgery. Methods. A 6-month follow-up study was performed among patients recruited from a Hospital in Northern Italy after their cancer-related surgery. Partecipants filled in a battery of questionnaires at baseline and at 6-month follow-up. Measurements were on job stress, work engagement, quality of life, anxiety, depression and resilience. Moreover, sociodemographic, clinical and work-related data were collected. Univariate and multivariate analyses were performed. Results. We recruited a sample of 149 patients, whose 73.9% returned to work after surgery. The women who returned to work were more likely to be not in a relationship, nor to have children. Also, they were not treated by chemiotherapy, and had higher scores in expectations of job changes after illness, RTW expectations, perception of physical quality of life and psychological resilience. In the multivariate model, chemiotherapy and women’s RTW expectations resulted as significant predictors of RTW at 6-month after BC surgery. Conclusion Most patients returned to work within first 6 months from breast surgery. Return to work was influenced by chemiotherapy and RTW expectations at baseline. A carefully individual screening of risk factors at baseline can prevent from occupational disability and long sickness absence.

Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance

Early outbreak detection is a key aspect in the containment of infectious diseases, as it enables the identification and isolation of infected individuals before the disease can spread to a larger population. Instead of detecting unexpected increases of infections by monitoring confirmed cases, syndromic surveillance aims at the detection of cases with early symptoms, which allows a more timely disclosure of outbreaks. However, the definition of these disease patterns is often challenging, as early symptoms are usually shared among many diseases and a particular disease can have several clinical pictures in the early phase of an infection. As a first step toward the goal to support epidemiologists in the process of defining reliable disease patterns, we present a novel, data-driven approach to discover such patterns in historic data. The key idea is to take into account the correlation between indicators in a health-related data source and the reported number of infections in the respective geographic region. In an preliminary experimental study, we use data from several emergency departments to discover disease patterns for three infectious diseases. Our results show the potential of the proposed approach to find patterns that correlate with the reported infections and to identify indicators that are related to the respective diseases. It also motivates the need for additional measures to overcome practical limitations, such as the requirement to deal with noisy and unbalanced data, and demonstrates the importance of incorporating feedback of domain experts into the learning procedure.

Judgment and Prevention of Urinary Tract Injury in Gynecological Surgery Based on Data Mining

In this paper, a data mining-enabled model is developed to analyze the case-related data of 39 patients with urinary tract injury who underwent laparoscopic surgery in a certain hospital from 2012 to 2017. Statistics on the history and characteristics of the case data summarized and analyzed the causes of urinary tract injury and the urinary system. The relationship between the occurrence of injury and the type of surgery and the treatment and preventive measures taken for urinary tract injury during and after surgery are summarized. The statistical method with SPSS16.0 statistical software was used to analyze the data of this study, and the X2 test was used to compare the rates. The differences of P ≤ 0.05 and P ≤ 0.01 were statistically significant. Laparoscopic surgery in gynecology is a minimally invasive technique, but it is still accompanied by the possibility of complications. During the experimental setup and implementation, we have observed that among 8742 cases of laparoscopic surgery complicated by urinary tract injury, there were 39 cases with a rate of 0.45%. In the past five years, the incidence of urinary tract injury in gynecological surgery in our country has increased year by year, and the number of cases of urinary tract injury has also increased year by year. Through analysis, it is found that the cause of the injury is related to the level of surgery, pelvic adhesion, and energy equipment. Based on the above problems, according to the clinical data of patients with urinary tract injury complicated by gynecological surgery in the hospital, the relevant factors of gynecological surgery complicated by urinary tract injury are analyzed to improve the awareness of urinary tract protection and prevention of injury during the operation and preventive measures are actively taken to avoid medical treatment.

Scoping Review of Oral Health-Related Birth Cohort Studies: Toward a Global Consortium

The multidisciplinary nature and long duration of birth cohort studies allow investigation of the relationship between general and oral health and indicate the most appropriate stages in life to intervene. To date, the worldwide distribution of oral health-related birth cohort studies (OHRBCSs) has not been mapped, and a synthesis of information on methodological characteristics and outcomes is not available. We mapped published literature on OHRBCSs, describing their oral health-related data and methodological aspects. A 3-step search strategy was adopted to identify published studies using PubMed, Embase, Web of Science, and OVID databases. Studies with baseline data collection during pregnancy or within the first year of life or linked future oral health data to exposures during either of these 2 life stages were included. Studies examining only mothers' oral health and specific populations were excluded. In total, 1,721 articles were suitable for initial screening of titles and abstracts, and 528 articles were included in the review, identifying 120 unique OHRBCSs from 34 countries in all continents. The review comprised literature from the mid-1940s to the 21st century. Fifty-four percent of the OHRBCSs started from 2000 onward, and 75% of the cohorts were from high-income and only 2 from low-income countries. The participation rate between the baseline and the last oral health follow-up varied between 7% and 93%. Ten cohorts that included interventions were mostly from 2000 and with fewer than 1,000 participants. Seven data-linkage cohorts focused mostly on upstream characteristics and biological aspects. The most frequent clinical assessment was dental caries, widely presented as decayed, missing, and filled teeth (DMFT/dmft). Periodontal conditions were primarily applied as isolated outcomes or as part of a classification system. Socioeconomic classification, ethnicity, and country- or language-specific assessment tools varied across countries. Harmonizing definitions will allow combining data from different studies, adding considerable strength to data analyses; this will be facilitated by forming a global consortium.

Water quality prediction based on Naïve Bayes algorithm

In the fast-changing world with increased water demand, water pollution, environmental problems, and related data, information on water quality and suitability for any purpose should be prompt and reliable. Traditional approaches often fail in the attempt to predict water quality classes and new ones are needed to handle a large amount or missing data to predict water quality in real-time. One of such approaches is machine-learning (ML) based prediction. This paper presents the results of the application of the Naïve Bayes, a widely used ML method, in creating the prediction model. The proposed model is based on nine water quality parameters: temperature, pH value, electrical conductivity, oxygen saturation, biological oxygen demand, suspended solids, nitrogen oxides, orthophosphates, and ammonium. It is created in software Netica and tested and verified using the data covering the period 2013–2019 from five locations in Vojvodina Province, Serbia. Forty-eight samples are used to train the model. Once trained, the Naïve Bayes model correctly predicted the class of water sample in 64 out of 68 cases, including cases with missing data. This recommends it as a trustful tool in the transition from traditional to digital water management.

Population-nosological research biobank of the National Medical Research Center for Therapy and Preventive Medicine: analysis of biosamples, principles of collecting and storing information

Aim. To analyze the structure of clinical data, as well as the principles of collecting and storing related data of the biobank of the National Medical Research Center for Therapy and Preventive Medicine (hereinafter Biobank).Material and methods. The analysis was carried out using the documentation available in the Biobank, as well as the databases used in its work. The paper presents clinical data on biosamples available in the Biobank as of August 18, 2021.Results. At the time of analysis, the Biobank had 373547 samples collected from 54192 patients within 37 research projects. The article presents the analysis of data representation and quantitative assessment of the presence/absence of common diagnoses in clinical projects. Approaches to documenting clinical information associated with biological samples stored in the Biobank were assessed. The methods and tools used for standardization and automation of processes used in the Biobank were substantiated.Conclusion. The Biobank of the National Medical Research Center for Therapy and Preventive Medicine is the largest research biobank in Russia, which meets all modern international requirements and is one of the key structures that improve the research quality and intensify their conduct both within the one center and in cooperation with other biobanks and scientific institutions. The collection and systematic storage of clinical abstracts of biological samples is an integral and most important part of the Biobank’s work.

Guidelines for genetic testing of healthy adults who deposit samples and related data in bioresource collections and biobanks

Currently, a significant part of research in the fields of human and medical genetics is carried out using tissue samples, genealogical, population, medical and personal data. Their use is of particular relevance in the “genome era”, since only joint analysis of genomic data and health status of the population is crucial for understanding how genes are associated with health and disease. Genetic studies of adults without symptoms of diseases are carried out to obtain data on a possible predisposition to multifactorial diseases, to establish the carrier status of autosomal recessive mutations as part of preconception care and to assess individual sensitivity to drugs. In addition, healthy individuals can be tested to detect an inherited disease at presymptomatic stage. This situation increasingly emphasizes the importance of storing data on genome sequencing or any other patient tests for subsequent data reanalysis, as well as their safety, including biosamples from an individual and one’s family. The review article, based on international experience, summarizes guidelines for genetic testing of healthy individuals. The options for storing biological samples and related data are considered.