Assessing colposcopic accuracy for high‐grade squamous intraepithelial lesion detection: a retrospective, cohort study

Background Inappropriate management of high-grade squamous intraepithelial lesions (HSIL) may be the result of an inaccurate colposcopic diagnosis. The aim of this study was to assess colposcopic performance in identifying HSIL+ cases and to analyze the associated clinical factors. Methods Records from 1130 patients admitted to Shenzhen Maternal and Child Healthcare Hospital from 12th January, 2018 up until 30th December, 2018 were retrospectively collected, and included demographics, cytological results, HPV status, transformation zone type, number of cervical biopsy sites, colposcopists’ competencies, colposcopic impressions, as well as histopathological results. Colposcopy was carried out using 2011 colposcopic terminology from the International Federation of Cervical Pathology and Colposcopy. Logistic regression modelling was implemented for uni- and multivariate analyses. A forward stepwise approach was adopted in order to identify variables associated with colposcopic accuracy. Histopathologic results were taken as the comparative gold standard. Results Data from 1130 patient records were collated and analyzed. Colposcopy was 69.7% accurate in identifying HSIL+ cases. Positive predictive value, negative predictive value, sensitivity and specificity of detecting HSIL or more (HSIL+) were 35.53%, 64.47%, 42.35% and 77.60%, respectively. Multivariate analysis highlighted the number of biopsies, cytology, and transformation zone type as independent factors. Age and HPV subtype did not appear to statistically correlate with high-grade lesion/carcinoma. Conclusion Evidence presented here suggests that colposcopy is only 69.7% accurate at diagnosing HSIL. Even though not all HSIL will progress into cancer it is considered pre-cancerous and therefore early identification will save lives. The number of biopsies, cytology and transformation zone type appear to be predictors of misdiagnosis and therefore should be considered during clinical consultations and by way of further research.

Perfil epidemiológico da infecção por COVID-19 em receptores de transplante renal de um centro do nordeste do Brasil e fatores preditivos de mortalidade

Objetivos: Investigar a mortalidade por COVID-19 em receptores de transplante renal (TR) e os fatores associados ao maior risco de óbito. Métodos: Os receptores de TR com suspeita de COVID-19 foram monitorados por nefrologistas de março a dezembro de 2020, através de contato telefônico diário, sendo registrados dados clinico-laboratoriais-evolutivos referentes à COVID-19. Uma análise multivariada (regressão logística, forward stepwise) para pesquisa de fatores preditivos de óbito foi realizada. Resultados: Foram confirmados 96 casos de COVID-19 (RT-PCR ou sorologia COVID), sendo 55,2% masculino, idade média 49,1 anos, 90,6% doador falecido e mediana do tempo de TR de 5,2 anos. As comorbidades mais frequentes foram HAS (76%), diabetes (37,5%) e doença cardiovascular (14,6%). A imunossupressão mais utilizada foi tacrolimo/micofenolato (66,7%) e prednisona (69,8%). Em 39,6% dos casos foi necessário internamento, sendo 18,8% em UTI. Em relação aos desfechos, 87,5% ficaram livres da doença e 12,5% foram a óbito. Na análise multivariada, associaram-se à maior chance de óbito: o tempo de transplante superior a 5,7 anos e a idade do receptor superior a 55 anos, enquanto uma menor creatinina basal associou-se à menor chance de mortalidade Conclusão: A mortalidade por COVID-19 em receptores de TR foi superior à da população geral, mas inferior ao descrito em outros centros de transplante. O tempo de transplante e a idade do receptor foram preditivos de óbito nesta população, enquanto uma menor creatinina basal associou-se à menor mortalidade.

Investigating the interpretability of fetal status assessment using antepartum cardiotocographic records

Background Cardiotocography (CTG) interpretation plays a critical role in prenatal fetal monitoring. However, the interpretation of fetal status assessment using CTG is mainly confined to clinical research. To the best of our knowledge, there is no study on data analysis of CTG records to explore the causal relationships between the important CTG features and fetal status evaluation. Methods For analyses, 2126 cardiotocograms were automatically processed and the respective diagnostic features measured by the Sisporto program. In this paper, we aim to explore the causal relationships between the important CTG features and fetal status evaluation. First, we utilized data visualization and Spearman correlation analysis to explore the relationship among CTG features and their importance on fetal status assessment. Second, we proposed a forward-stepwise-selection association rule analysis (ARA) to supplement the fetal status assessment rules based on sparse pathological cases. Third, we established structural equation models (SEMs) to investigate the latent causal factors and their causal coefficients to fetal status assessment. Results Data visualization and the Spearman correlation analysis found that thirteen CTG features were relevant to the fetal state evaluation. The forward-stepwise-selection ARA further validated and complemented the CTG interpretation rules in the fetal monitoring guidelines. The measurement models validated the five latent variables, which were baseline category (BCat), variability category (VCat), acceleration category (ACat), deceleration category (DCat) and uterine contraction category (UCat) based on fetal monitoring knowledge and the above analyses. Furthermore, the interpretable models discovered the cause factors of fetal status assessment and their causal coefficients to fetal status assessment. For instance, VCat could predict BCat, and UCat could predict DCat as well. ACat, BCat and DCat directly affected fetal status assessment, where ACat was the important causal factor. Conclusions The analyses revealed the interpretation rules and discovered the causal factors and their causal coefficients for fetal status assessment. Moreover, the results are consistent with the computerized fetal monitoring and clinical knowledge. Our approaches are conducive to evidence-based medical research and realizing intelligent fetal monitoring.

GEDI: an R package for integration of transcriptomic data from multiple high-throughput platforms

Transcriptomic data is often expensive and difficult to generate in large cohorts in comparison to genomic data and therefore is often important to integrate multiple transcriptomic datasets from both microarray and next generation sequencing (NGS) based transcriptomic data across similar experiments or clinical trials to improve analytical power and discovery of novel transcripts and genes. However, transcriptomic data integration presents a few challenges including re-annotation and batch effect removal. We developed the Gene Expression Data Integration (GEDI) R package to enable transcriptomic data integration by combining already existing R packages. With just four functions, the GEDI R package makes constructing a transcriptomic data integration pipeline straightforward. Together, the functions overcome the complications in transcriptomic data integration by automatically re-annotating the data and removing the batch effect. The removal of the batch effect is verified with Principal Component Analysis and the data integration is verified using a logistic regression model with forward stepwise feature selection. To demonstrate the functionalities of the GEDI package, we integrated five bovine endometrial transcriptomic datasets from the NCBI Gene Expression Omnibus. The datasets included Affymetrix, Agilent and RNA-sequencing data. Furthermore, we compared the GEDI package to already existing tools and found that GEDI is the only tool that provides a full transcriptomic data integration pipeline including verification of both batch effect removal and data integration.

Pendekatan Model Machine Learning dalam Pemeringkatan Status Sosial Ekonomi Rumah Tangga di Indonesia

Metode yang digunakan untuk pemeringkatan status sosial ekonomi rumah tangga Basis Data Terpadu adalah dengan memprediksi nilai pengeluaran rumah tangga dengan metode Proxy Mean Testing (PMT). Secara umum metode ini merupakan model prediksi dengan menggunakan teknik regresi. Pilihan model statistik yang digunakan adalah forward-stepwise. Dalam praktiknya diasumsikan bahwa variabel prediktor yang digunakan dalam PMT memiliki korelasi linier dengan variabel pengeluaran. Penelitian ini mencoba menerapkan pendekatan machine learning sebagai alternatif metode prediksi selain model forward-stepwise. Model dibangun menggunakan beberapa algoritma machine learning seperti Multivariate Adaptive Regression Splines (MARS), K-Nearest Neighbors, Decision Tree, dan Bagging. Hasil pemodelan menunjukkan bahwa model machine learning menghasilkan nilai rata-rata inclusion error (IE) lebih rendah dibandingkan nilai rata-rata exclusion error (EE). Model machine learning bekerja efektif dalam mengurangi IE namun belum cukup sensitif dalam mengurangi EE. Nilai rata-rata IE model machine learning sebesar 0,21 sedangkan nilai rata-rata IE model PMT sebesar 0,29.

Social Media Behaviors and Experiences During the COVID-19 Pandemic: Associations With Anxiety, Depression, and Stress

The majority of research concerning public health crises and social media platforms has focused on analyzing the accuracy of information within social media posts. The current exploratory study explored social media users’ specific social media behaviors and experiences during the early weeks of the COVID-19 pandemic and whether these behaviors and experiences related to anxiety, depression, and stress. Data were collected March 21–31, 2020 from adults in the United States (<em>N</em> = 564) through snowball sampling on social media sites and Prime Panels. Online surveys included questions regarding social media use during the pandemic and the Depression Anxiety and Stress Scales (DASS). Forward stepwise modeling procedures were used to build three models for anxiety, stress, and depression. Participants who actively engaged with COVID-19 social media content were more likely to experience higher anxiety. Those who had emotional experiences via social media and used social media to connect during the pandemic were susceptible to higher levels of stress and depression. The current study suggests that during the pandemic specific behaviors and experiences via social media were related to anxiety, stress, and depression. Thus, limiting time spent on social media during public health crises may protect the mental health of individuals.

A Systematic Study of the Antioxidant Capacity of Humic Substances against Peroxyl Radicals: Relation to Structure

Humic substances (HS) are natural supramolecular systems of high- and low-molecular-weight compounds with distinct immunomodulatory and protective properties. The key beneficial biological activity of HS is their antioxidant activity. However, systematic studies of the antioxidant activity of HS against biologically relevant peroxyl radicals are still scarce. The main objective of this work was to estimate the antioxidant capacity (AOC) of a broad set of HS widely differing in structure using an oxygen radical absorption capacity (ORAC) assay. For this purpose, 25 samples of soil, peat, coal, and aquatic HS and humic-like substances were characterized using elemental analysis and quantitative 13C solution-state NMR. The Folin–Ciocalteu method was used to quantify total phenol (TP) content in HS. The determined AOC values varied in the range of 0.31–2.56 μmol Trolox eqv. mg−1, which is close to the values for ascorbic acid and vitamin E. Forward stepwise regression was used to reveal the four main factors contributing to the AOC value of HS: atomic C/N ratio, content of O-substituted methine and methoxyl groups, and TP. The results obtained clearly demonstrate the dependence of the AOC of HS on both phenolic and non-phenolic moieties in their structure, including carbohydrate fragments.

Helicobacter pylori Infection in Children with Phenylketonuria Does Not Depend on Metabolic Control and Is Not More Frequent Than in Healthy Subjects—A Cross-Sectional Study

In a small preliminary study, phenylketonuria and poor metabolic control were suggested as risk factors for Helicobacter pylori infection in children as detected with an antigen stool test. We aimed to determine Helicobacter pylori prevalence in an adequately sized group of individuals with phenylketonuria and healthy subjects using the standard gold test (urea breath test). Further, we correlated Helicobacter pylori infection with metabolic control. The study comprised 103 individuals with phenylketonuria and 103 healthy subjects on whom a 13C urea breath test was performed. Blood phenylalanine levels in the preceding year were analysed. The infection rate did not differ between individuals with phenylketonuria and healthy subjects (10.7% vs 15.5%; p = 0.41). The frequency of testing and phenylalanine concentrations of Helicobacter pylori-positive and Helicobacter pylori-negative patients with phenylketonuria did not differ (p = 0.92 and p = 0.54, respectively). No associations were detected for body mass index or metabolic control. Forward stepwise regression models revealed that age (p = 0.0009–0.0016) was the only independent correlate of Helicobacter pylori infection with a relatively low fraction of the variability of the condition being explained (adjR2 = 0.0721–0.0754; model p = 0.020–0.023). In conclusion, Helicobacter pylori infection in phenylketonuria is not more frequent than in the general population. Moreover, it does not depend on metabolic control.

Social Capital Values as the Strengthening Elements in the Rural Small Industry

This research aims at revealing various practices of social capital values to strengthen rural small industry. The practices which emerge from the interaction among farmers, big vendor, tibo, roasted nut business owner and consumers lead to different forms of social capital values. This research uses qualitative research method which involves a study case. The data are analyzed using forward stepwise model from Spradley, which formulates the findings from the empirical research. The research findings reveal various social capital values embedded in local tradition of Minahasa known as Mapalus. It refers to a form of cooperation which grow and develop among the Minahasans. Mapalus includes various social activities as follows (1) Mendu impero’ongan, a community service performed by the villagers; (2) Berantang, providing help for the bereaved family; (3) Sumakey, celebrating certain occasions together. Mapalus also exists in economic and financial activities such as: (1) Ma’endo. It refers to communal activities to cultivate the field or to renovate houses; and (2) Pa’ando. It is a financial activity in a form social gathering known as ‘arisan’. This cultural value is an important social capital for rural society to support their economic activities and to strengthen the rural small industry.