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This research aimed to improve students’ critical thinking skills through make a match model assisted by puzzle media for elementary school students in the even semester of the academic year 2019/ 2020. This research was a Classroom Action Research (CAR) conducted in 2 cycles by using make a match as a learning method and puzzle as a learning medium. The subjects of this study were 26 students at grade V SD 3 Adiwarno, which consisted of 14 male students and 12 female students. The research data were collected by using the learning outcome test in the form of description question and non-test technique (interviews, observation, and documntation). The data from the test were analyzed descriptively and quantitatively. At the pre-cycle stage consisting of PPKn and Social Science, the result of critical thinking test obtained an average of classical learning completeness of 1846 with a percentage of 44% (fairly good category). Then, the result of classical learning completeness obtained in cycle Iwas 2025 with a percentage of 73% in good category. Moreover, the result of classical learning completeness in cycle II was 2125 with a percentage of 85% in very good category. Thus, it could be concluded that make a match model assisted by puzzlemedia improved stuednts’ critical thinking skills

Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization. We propose two DNN-based covariate-aware harmonization approaches: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three continents with a total of 2787 participants and 10,085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat. Code for DeepResBat can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/harmonization/An2024_DeepResBat., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: The long-term outcomes (≥ 2 years of follow-up) of volar locked plate (VLP) fixation versus closed reduction and casting (CRC) for the treatment of displaced distal radial fractures (DRFs) remain unclear. This study aimed to conduct a meta-analysis comparing the long-term clinical outcomes of VLP and CRC in elderly patients (aged ≥ 60 years)., Methods: A comprehensive search of PubMed, Web of Science, and Cochrane Library was performed to identify studies comparing the long-term outcomes of VLP and CRC for DRFs. Only randomized controlled trials (RCTs) with a mean follow-up duration of at least 2 years and participants aged 60 years or older were included. The risk of bias in the included studies was assessed. The primary outcome measure was the Patient-Rated Wrist Evaluation (PRWE). Secondary outcomes included the Disabilities of the Arm, Shoulder, and Hand (DASH) questionnaire, EuroQol 5 Dimension 5-level score (EQ-5D-5 L), grip strength, and incidence of reoperation., Results: Four RCTs, comprising 423 patients, were included. The meta-analysis revealed that VLP fixation was associated with significantly improved outcomes compared to CRC in terms of PRWE score (pooled mean difference: -6.21; 95% CI: -10.28 to -2.15; p = 0.003), DASH score (pooled mean difference: -8.18; 95% CI: -13.35 to -3.01; p = 0.002), and grip strength (pooled mean difference: -6.63; 95% CI: 0.25 to 13.01; p = 0.04). There were no significant differences in EQ-5D-5 L score (95% CI: -0.08 to 0.05; p = 0.74) or incidence of reoperation (RR = 0.56; 95% CI: 0.22 to 1.42; p = 0.22)., Conclusions: A two-year follow-up of displaced DRFs in elderly patients showed no significant long-term clinical advantage of VLP fixation over CRC., Competing Interests: Declarations. Ethical approval: Not applicable. Consent for publication: All authors have stated for their consent for publication. CRD: 42024532685. Final statements: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Accurate Alzheimer's Disease (AD) progression prediction is essential for early intervention. The TADPOLE challenge, involving 92 algorithms, used multimodal biomarkers to predict future clinical diagnosis, cognition, and ventricular volume. The winning algorithm, FROG, utilized a Longitudinal-to-Cross-sectional (L2C) transformation to convert variable longitudinal histories into fixed-length feature vectors, which contrasted with most existing approaches that fitted models to entire longitudinal histories, e.g., AD Course Map (AD-Map) and minimal recurrent neural networks (MinimalRNN). The TADPOLE challenge only utilized the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. To evaluate FROG's generalizability, we trained it on the ADNI dataset and tested it on three external datasets covering 2,312 participants and 13,200 timepoints. We also introduced two FROG variants. One variant, L2C feedforward neural network (L2C-FNN), unified all XGBoost models used by the original FROG with an FNN. Across external datasets, L2C-FNN and AD-Map were the best for predicting cognition and ventricular volume. For clinical diagnosis prediction, L2C-FNN was the best, while AD-Map was the worst. L2C-FNN compared favorably with other approaches regardless of the number of observed timepoints, and when predicting from 0 to 6 years into the future, underscoring its potential for long-term dementia progression prediction. Pretrained ADNI models are publicly available: GITHUB_LINK.

A primary aim of computational psychiatry is to establish predictive models linking individual differences in brain functioning with symptoms. In particular, cognitive impairments are transdiagnostic, treatment resistant, and associated with poor outcomes. Recent work suggests that thousands of participants may be necessary for the accurate and reliable prediction of cognition, questioning the utility of most patient collection efforts. Here, using a transfer learning framework, we train a model on functional neuroimaging data from the UK Biobank to predict cognitive functioning in three transdiagnostic samples (ns = 101 to 224). We demonstrate prediction performance in all three samples comparable to that reported in larger prediction studies and a boost of up to 116% relative to classical models trained directly in the smaller samples. Critically, the model generalizes across datasets, maintaining performance when trained and tested across independent samples. This work establishes that predictive models derived in large population-level datasets can boost the prediction of cognition across clinical studies.

Background: The eastern European and central Asian (EECA) region has the fastest growing HIV epidemic globally. We aimed to identify how HIV resources could be allocated for maximum health impact., Methods: Between Aug 1 and Dec 23, 2022, allocative efficiency analyses were undertaken for 12 countries in the EECA region (Albania, Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kosovo, Kyrgyzstan, Moldova, Serbia, Tajikistan, and Uzbekistan) using HIV epidemic models developed with Optima HIV. Country models were calibrated to demographic, epidemiological, and programmatic data and validated by national teams. Three scenarios were projected from 2023 to 2030: status quo (continued 2021 spending on HIV programmes); optimised allocation of different spending envelopes to minimise HIV infections and deaths; and achieving 95-95-95 UNAIDS targets by 2030., Findings: Aggregated across the 12 models, HIV infections attributable to sexual transmission were estimated to surpass those attributable to transmission through injecting drugs in 2018, with male-to-male sexual transmission accounting for a continuously increasing share. In the status quo scenario, there were an estimated 111 520 (95% CI 28 960-208 270) new HIV infections and 34 530 (17 280-57 410) HIV-related deaths between 2023 and 2030. Aggregated optimisation results suggest that 35 860 (32%) of 111 520 new HIV infections and 9170 (27%) of 34 530 HIV-related deaths could be averted from 2023 to 2030 compared with the status quo, by prioritising antiretroviral therapy and targeted key population programmes. For ten countries, achieving 95% diagnosis was projected to not be possible with the current budget envelope, and for seven countries, this target could require more than three times the current spending. Compared with the status quo, achieving 95-95-95, or as close as possible, could avert 70 880 (64%) of 111 520 new HIV infections and 18 890 (55%) of 34 530 HIV-related deaths from 2023 to 2030., Interpretation: Targeted key population programmes should remain high priorities in the EECA region. Achieving 95-95-95 will require more emphasis on implementing appropriate modes of service delivery that reduce the gap in diagnosis and treatment coverage for people living with HIV., Funding: The Global Fund to Fight AIDS, Tuberculosis and Malaria., Translation: For the Russian translation of the summary see Supplementary Materials section., Competing Interests: Declaration of interests SZ and CM are employed by the funder of this study (The Global Fund to Fight AIDS, Tuberculosis and Malaria). All other authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)

Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization. We propose two DNN-based covariate-aware harmonization approaches: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three continents with a total of 2787 participants and 10085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat. Code for DeepResBat can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/harmonization/An2024_DeepResBat., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Individualized phenotypic prediction based on structural MRI is an important goal in neuroscience. Prediction performance increases with larger samples, but small-scale datasets with fewer than 200 participants are often unavoidable. We have previously proposed a "meta-matching" framework to translate models trained from large datasets to improve the prediction of new unseen phenotypes in small collection efforts. Meta-matching exploits correlations between phenotypes, yielding large improvement over classical machine learning when applied to prediction models using resting-state functional connectivity as input features. Here, we adapt the two best performing meta-matching variants ("meta-matching finetune" and "meta-matching stacking") from our previous study to work with T1-weighted MRI data by changing the base neural network architecture to a 3D convolution neural network. We compare the two meta-matching variants with elastic net and classical transfer learning using the UK Biobank (N = 36,461), Human Connectome Project Young Adults (HCP-YA) dataset (N = 1,017) and HCP-Aging dataset (N = 656). We find that meta-matching outperforms elastic net and classical transfer learning by a large margin, both when translating models within the same dataset, as well as translating models across datasets with different MRI scanners, acquisition protocols and demographics. For example, when translating a UK Biobank model to 100 HCP-YA participants, meta-matching finetune yielded a 136% improvement in variance explained over transfer learning, with an average absolute gain of 2.6% (minimum = -0.9%, maximum = 17.6%) across 35 phenotypes. Overall, our results highlight the versatility of the meta-matching framework., Competing Interests: Competing Interests The authors declare no competing interests.

Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantly improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a necessity. We have previously proposed a "meta-matching" approach to translate prediction models from large datasets to predict new phenotypes in small datasets. We demonstrated large improvement of meta-matching over classical kernel ridge regression (KRR) when translating models from a single source dataset (UK Biobank) to the Human Connectome Project Young Adults (HCP-YA) dataset. In the current study, we propose two meta-matching variants ("meta-matching with dataset stacking" and "multilayer meta-matching") to translate models from multiple source datasets across disparate sample sizes to predict new phenotypes in small target datasets. We evaluate both approaches by translating models trained from five source datasets (with sample sizes ranging from 862 participants to 36,834 participants) to predict phenotypes in the HCP-YA and HCP-Aging datasets. We find that multilayer meta-matching modestly outperforms meta-matching with dataset stacking. Both meta-matching variants perform better than the original "meta-matching with stacking" approach trained only on the UK Biobank. All meta-matching variants outperform classical KRR and transfer learning by a large margin. In fact, KRR is better than classical transfer learning when less than 50 participants are available for finetuning, suggesting the difficulty of classical transfer learning in the very small sample regime. The multilayer meta-matching model is publicly available at GITHUB_LINK., Competing Interests: Competing Interests The authors declare no competing interests.

Background: Mathematical models are increasingly used to inform HIV policy and planning. Comparing estimates obtained using different mathematical models can test the robustness of estimates and highlight research gaps. As part of a larger project aiming to determine the optimal allocation of funding for HIV services, in this study we compare projections from five mathematical models of the HIV epidemic in South Africa: EMOD-HIV, Goals, HIV-Synthesis, Optima, and Thembisa., Methods: The five modelling groups produced estimates of the total population, HIV incidence, HIV prevalence, proportion of people living with HIV who are diagnosed, ART coverage, proportion of those on ART who are virally suppressed, AIDS-related deaths, total deaths, and the proportion of adult males who are circumcised. Estimates were made under a "status quo" scenario for the period 1990 to 2040. For each output variable we assessed the consistency of model estimates by calculating the coefficient of variation and examining the trend over time., Results: For most outputs there was significant inter-model variability between 1990 and 2005, when limited data was available for calibration, good consistency from 2005 to 2025, and increasing variability towards the end of the projection period. Estimates of HIV incidence, deaths in people living with HIV, and total deaths displayed the largest long-term variability, with standard deviations between 35 and 65% of the cross-model means. Despite this variability, all models predicted a gradual decline in HIV incidence in the long-term. Projections related to the UNAIDS 95-95-95 targets were more consistent, with the coefficients of variation below 0.1 for all groups except children., Conclusions: While models produced consistent estimates for several outputs, there are areas of variability that should be investigated. This is important if projections are to be used in subsequent cost-effectiveness studies., (© 2023. The Author(s).)

Resting-state functional connectivity (RSFC) is widely used to predict behavioral measures. To predict behavioral measures, representing RSFC with parcellations and gradients are the two most popular approaches. Here, we compare parcellation and gradient approaches for RSFC-based prediction of a broad range of behavioral measures in the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. Among the parcellation approaches, we consider group-average "hard" parcellations (Schaefer et al., 2018), individual-specific "hard" parcellations (Kong et al., 2021a), and an individual-specific "soft" parcellation (spatial independent component analysis with dual regression; Beckmann et al., 2009). For gradient approaches, we consider the well-known principal gradients (Margulies et al., 2016) and the local gradient approach that detects local RSFC changes (Laumann et al., 2015). Across two regression algorithms, individual-specific hard-parcellation performs the best in the HCP dataset, while the principal gradients, spatial independent component analysis and group-average "hard" parcellations exhibit similar performance. On the other hand, principal gradients and all parcellation approaches perform similarly in the ABCD dataset. Across both datasets, local gradients perform the worst. Finally, we find that the principal gradient approach requires at least 40 to 60 gradients to perform as well as parcellation approaches. While most principal gradient studies utilize a single gradient, our results suggest that incorporating higher order gradients can provide significant behaviorally relevant information. Future work will consider the inclusion of additional parcellation and gradient approaches for comparison., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Inc.)

Adolescence is a developmental phase where mental disorders typically manifest and where platforms for response (including schools and health services) change rapidly. However, data to inform public mental health responses are limited, including in countries like Myanmar which has a large adolescent population and where mental health has been identified as a priority of policy. In this paper we sought to systematically review the peer-reviewed and grey literature to determine (i) the prevalence of mental disorder among adolescents in Myanmar, (ii) determinants of mental disorder and (iii) interventions that have been implemented and evaluated. Nine publications met inclusion criteria (7 peer-reviewed and 2 grey literature) that included 7 publications reporting prevalence, 6 reporting correlates and one an intervention. The available data from the 2016 Global School-based Health Survey highlight that depression (27.2%) and suicidal ideation (9.4%) are prevalent in Myanmar, and these rates are substantially higher than regional averages. The limited available data on correlates identified violence and bullying, alcohol and substance use, and home, family and community security and cohesion as being closely related to mental health for adolescents. Only one study focussed on interventions and this found mindfulness meditation training to be an effective approach for young people whose parents were affected by HIV. These findings underscore the need to address adolescent mental health in Myanmar, but also to invest in better data collection efforts., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: By adulthood, gender inequalities in health and wellbeing are apparent. Yet, the timing and nature of gender inequalities during childhood and adolescence are less clear. We describe the emergence of gender inequalities in health and wellbeing across the first two decades of life., Methods: We focused on the 40 low-income and middle-income countries in Asia and the Pacific. A measurement framework was developed around four key domains of wellbeing across the first two decades: health, education and transition to employment, protection, and a safe environment. Specific measurement constructs were then defined by considering gender indicator frameworks, the Sustainable Development Goals, indicator frameworks for child and adolescent health and wellbeing, and key stakeholder input. Available data were then mapped to define 87 indicators, subsequently populated using databases (UN agencies and the Global Burden of Diseases, Injuries, and Risk Factors Study) and nationally representative surveys. Where possible, estimates in girls were compared with boys to report relative risks., Findings: Although son preference is evident in some settings-as shown by higher than expected male-to-female sex ratios at birth in India, Vietnam, and China (all >1·10 compared with an expected ratio of 1·05) and excess mortality of girl children in some South Asian and Pacific nations-it is during early adolescence where marked gender inequalities consistently emerged. Adolescent girls face considerable disadvantage in relation to sexual and reproductive health (notably in South Asia and the Pacific), with high rates of child marriage (≥30% of women aged 20-24 years married before 18 years in Bangladesh, Nepal, and Afghanistan), fertility (≥65 livebirths per 1000 girls in Nauru, Laos, Afghanistan, Nepal, Marshall Islands, Bangladesh, Vanuatu, and Papua New Guinea), and intimate partner violence (>20% in Timor Leste, Afghanistan, Pakistan, and Myanmar). Despite educational parity in many countries, females aged 15-24 years were less likely than males to be in education, employment, or training in 17 of 19 countries for which data were available. Compared with girls, adolescent boys experienced excess all-cause mortality and substantially higher mortality due to unintentional injury, interpersonal violence, alcohol and other drugs, and suicide, and higher prevalence of harmful drinking and tobacco smoking., Interpretation: These findings call for a focus on gender policy and programming in later childhood and early adolescence before gender inequalities become embedded., Funding: UNICEF., (Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Poor mental health is a leading contributor to the burden of disease experienced by adolescents, including in resource constrained settings. However, little is known about how adolescents in these countries conceptualise mental health and its determinants which is essential to informing effective responses. This study aimed to explore how adolescents in Indonesia (a populous and rapidly developing country) conceptualise mental health and what they identify as important determinants. Eight focus group discussions (FGDs) were conducted with 86 Indonesian adolescents (aged 16-18 years), sampled from schools and community settings from Jakarta and South Sulawesi. FGDs were recorded, transcribed, translated and thematically analysed. Mental health was recognised as a significant concern by adolescents in Indonesia. Good mental health was conceptualised as emotional wellbeing and happiness. By contrast, poor mental health was predominantly described in terms of substantial mental illness manifesting as behavioural and physical disturbance. Further, poor mental health only happened to 'other' people, with stigmatising views prevalent. Absent from the discussions were common symptoms of poor mental health (stress, loneliness, poor sleep) and common mental disorders (e.g. depression, anxiety) or a conceptualisation that reflected poor mental health to be a normal human experience. Discussions around determinants of poor mental health suggested that family connections (particularly with parents), school pressures, and adverse exposures on social media were important drivers of poor mental health, with religion also surfacing as an important determinant. In highlighting mental health as an important issue for Indonesian adolescents, this study provides a foundation for targeted responses., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Non-communicable diseases (NCDs) are the leading cause of morbidity and mortality globally, with the burden largely borne by people living in low- and middle-income countries. Adolescents are central to NCD control through the potential to modify risks and alter the trajectory of these diseases across the life-course. However, an absence of epidemiological data has contributed to the relative exclusion of adolescents from policies and responses. This paper documents the design of a study to measure the burden of metabolic syndrome (a key risk for NCDs) and poor mental health (a key outcome) amongst Indonesian adolescents. Using a mixed-method design, we sampled 16-18-year-old adolescents from schools and community-based settings across Jakarta and South Sulawesi. Initial formative qualitative enquiry used focus group discussions to understand how young people conceptualise mental health and body weight (separately); what they perceive as determinants of these NCDs; and what responses to these NCDs should involve. These findings informed the design of a quantitative survey that adolescents self-completed electronically. Mental health was measured using the Centre for Epidemiologic Studies Depression Scale-Revised (CESD-R) and Kessler-10 (both validated against formal psychiatric interview in a subsample), with the metabolic syndrome measured using biomarkers and anthropometry. The survey also included scales relating to victimisation, connectedness, self-efficacy, body image and quality of life. Adolescents were sampled from schools using a multistage cluster design, and from the community using respondent-driven sampling (RDS). This study will substantially advance the field of NCD measurement amongst adolescents, especially in settings like Indonesia. It demonstrates that high quality, objective measurement is acceptable and feasible, including the collection of biomarkers in a school-based setting. It demonstrates how comparable data can be collected across both in-school and out of school adolescents, allowing a more comprehensive measure of NCD burden, risk and correlates.

Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning., Method: The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms., Results: Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved., Conclusion: The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Objective: Clinically validate the SNIPER human papillomavirus (HPV) DNA assay for the detection of cervical intraepithelial neoplasia (CIN)2 or higher and CIN2 or higher in a prospective cross-sectional screening study in Guizhou Province, China., Methods: Between March and April, 2008, 1000 nonpregnant women aged 30 or older were recruited in Guizhou Province, China. Women positive by SNIPER or cytological examination were requested to return for follow-up. A biopsy of all colposcopically detected abnormalities was performed by quadrant. In normal quadrants, biopsies were obtained at the squamocolumnar junction (2-, 4-, 8-, and 10-o'clock positions depending on the quadrant). Samples were placed in 2 mL of saline solution and maintained between 2 degrees C and 30 degrees C for up to 1 week. One milliliter of this suspension was then prepared and tested. For polymerase chain reaction amplification, a pool of HPV primers was designed to amplify HPV DNA from 13 high-risk-HPV genotypes (types 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, and 68). Test characteristics were calculated according to standard definitions., Results: One thousand women were screened; 175 tested HPV positive, 36 women tested negative but had positive Papanicolaou test results. All but 21 (90%) returned for follow-up. Median age and proportions having CIN2 or higher and CIN3 or higher differed by HPV status. Twenty-five women had CIN2 or higher and 16 had CIN3 or higher. The SNIPER assay was 93.3% and 94% sensitive and 86% and 85% specific for the detection of CIN2 or higher and CIN3 or higher, respectively. The positive predictive value was 17.4 % and 9.9% for CIN2 or higher and CIN3 or higher, respectively. Negative predictive value approached 100% for CIN2 or higher and CIN3 or higher., Conclusion: The SNIPER assay is functionally competitive and in terms of cost holds an advantage over Hybrid Capture 2 in a Chinese healthcare market, and potentially others, around the world.

Objectives: To determine the sensitivity and specificity of optical coherence tomography (OCT) as an adjunct to unaided visual inspection using acetic acid (VIA) in the detection of cervical intraepithelial neoplasia 2 (CIN 2) in a real-time clinical evaluation., Background: This clinical study was a prospective cross-sectional comparative trial that screened 1000 patients (aged 30-50 years) in a low-resource setting. Women with abnormal cervical cytology or positive human papillomavirus (HPV) tests were referred for further evaluation including VIA, OCT imaging, colposcopy, and cervical biopsies., Methods: The VIA diagnoses were coded by quadrant. The OCT was then performed in all VIA-positive areas and at the squamocolumnar junction in all 4 quadrants. All patients were colposcoped; assessed by quadrant with biopsies at 2, 4, 8, and 10 o'clock; all abnormal areas were biopsied; and endocervical curettage was performed. Data were analyzed using generalized estimating equations and logistic regression., Results: Of the 1000 patients, 175 (17.5%) were HPV positive, 93 (9.3%) had abnormal cervical cytology greater than or equal to atypical squamous cells of undetermined significance, and 211 (21.1%) were either HPV positive or had abnormal cytology. The VIA, OCT, colposcopy, and biopsies were completed on 183 (86.7%) of 211 women. For VIA alone, the sensitivity and specificity in detecting lesions greater than or equal to CIN 2 was 43% and 96%. With the addition of OCT, the sensitivity increases to 62% with a specificity of 80%., Conclusions: With the addition of OCT, the sensitivity of VIA increased in all analyses for the detection of greater than or equal to CIN II, with a loss in specificity. We hope that the potential of this technology will be realized when a computer algorithm is generated to aid in image interpretation.

Objective: To investigate the prevalence of high-risk human papillomavirus (HPV) and incidence of cervical intraepithelial neoplasia (CIN) in female populations in Shenzhen, Guangdong Province, China., Methods: Totally 1137 women aged 15-59 from Shahe Community, Nanshan District, Shenzhen were investigated for cervical cancer during an population-based epidemiological screening from November 2004 to December 2004. Visual inspection with acetic acid (VIA), colposcopy, liquid-based cytology test (LCT), and hybrid capture 2 (HC-) were performed to detect the high-risk HPV types in cervical secretions. Biopsy under colposcope was performed in women who were HPV-positive with LCT >or= atypical squamous cells of undetermined sign (ASCUS) or HPV-negative with LCT >or= low grade squamous intraepithelial lesion (LSIL), with the pathological results as the golden standards., Results: The detection rate of high-risk HPV-DNA was 14.0%. HPV detection rates in 15-24, 25-29, 30-34, 35-39, 40-44, 45-49, and 50-59 age groups were 15.5%, 17.7%, 12.6%, 8.8%, 10.2%, 15.3%, and 21.0%, respectively (P < 0.05). HPV detection rates in 25-29 years group and 50-59 years group were significantly higher than those in other groups (P < 0.05) and 35-39 group had the lowest detection rate. The curve of HPV infection rates in all groups was 'V' type. The overall incidence of CIN was 4.4%. The incidences of CIN , CIN , and CIN were 3.2%, 1.0%, and 0.3%, respectively, in which the incidence of CIN was significantly higher than those of CIN and . HPV detection rates increased with cervical lesion grades, which in >or=CIN groups and normal group were 100.0% and 8.3%, respectively. No cervical cancer was identified in this research. The sensitivities of VIA, colposcopy, LCT, and HC-II for high-risk HPV screening were 35.7%, 50.0%, 92.9%,and 100%, respectively, in detecting high-grade squamous intraepithelial lesion (HSIL), the specificities of these four methods were 96.0%, 87.2%, 88.4%, and 86.9%, respectively. Satisfactory negative predictive values were obtained for all methods., Conclusions: HPV infection is the main risk factor for CIN. Cervical cancer among female populations in Shenzhen is still in early stages. Prevention of HPV infection and treatment of CIN are key for the prevention of cervical cancer.

Objective: Determine the sensitivity and specificity of optical coherence tomography (OCT) as an adjunct to colposcopy in the detection of cervical intraepithelial neoplasia (CIN) grade 2 or higher in a real-time clinical evaluation., Background: Optical coherence tomography (OCT) uses infrared light similar to ultrasound pulse-echo imaging. Image resolution is optimal in the 1-to-3-mm range. This study is the third in our series of OCT investigations and our first real-time clinical trial. The study was conducted at the Peking University Shenzhen Hospital, Shenzhen, China., Methods: Nonpregnant women 18 years or older with abnormal cervical cytologic findings or a positive high-risk human papillomavirus test result were recruited. Women were assessed; and diagnoses, recorded by cervical quadrant first with colposcopy, followed by colposcopic directed OCT. A biopsy of the abnormal areas was performed. In normal quadrants, biopsy specimens were obtained at the 2-, 4-, 8-, and 10-o'clock positions at the squamocolumnar junction depending on the quadrant. An endocervical curettage was also done. Individual OCT diagnoses were paired with colposcopic impressions and biopsy specimens to assess its role as a paired secondary screen. Data were analyzed using generalized estimating equations to control for correlation within a woman., Results: One thousand two hundred thirty-seven paired diagnoses from 299 women were analyzed. Median age was 36 years. Ninety-six women (8%) had a diagnosis of CIN 2 or higher. Evaluation by quadrant showed that the sensitivity for CIN 2 or higher decreased by adding OCT to colposcopy, but the specificity increased from 83% to 93%., Conclusions: We continue to try to improve sensitivity by improving the near-infrared light source, decreasing the scan time to 8 frames per second, and using a larger diameter (5 mm) fiberoptic probe with a newly designed application specific probe sheath.

Objective: To investigate the significance of genomic amplification of the telomerase RNA component (TERC) gene to serve as a genetic biomarker in the screening of cervical lesions., Methods: A total of 715 cases were recruited, with liquid-based cytology diagnosis as normal (n = 347), atypical squamous cells of undetermined significance (ASCUS, n = 180), atypical squamous cells cannot exclude a high-grade lesion (ASC-H, n = 13), low-grade squamous intraepithelial lesions (LSIL, n = 115), high-grade squamous intraepithelial lesions (HSIL, n = 59) and atypical glandular cells (AGC, n = 1). The remaining cervical cells in the cytological preserving fluid were analyzed using a two-color fluorescence in situ hybridization (FISH) probe targeted to chromosome 3q26 containing TERC gene. The TERC gene findings were compared to the cytological and histological detected results, as well as high-risk human papillomavirus (HPV) detected results., Results: Genomic amplification of TERC gene was found in 5.8% of normal specimens, 22.2% of ASCUS, 30.8% of ASC-H, 27.8% of LSIL, 86.4% of HSIL and 1/1 of AGC. The positive rate was significantly lower in normal, ASCUS, ASC-H and LSIL compared with HSIL (all P < 0.01). Significantly more cells with genomic amplification of TERC gene were found in cervical intraepithelial lesion (CIN) II-III than CINI (77.8% vs. 9.3%), as well as invasive cervical cancer (96.7% vs. 9.3%), both P < 0.01. The rate of TERC gene amplification was higher in HPV positive patients (33.5%) than in HPV negative patients (5.2%, P < 0.01). The sensitivity of TERC gene amplification was significantly higher than that of cytological screening (81.88% vs. 36.96%, P < 0.01) in the differentiation of CINII or higher and CINI or lower diseases, its specificity was higher than high-risk HPV test (93.32% vs. 33.93%, P < 0.01) and positive prediction value (81.29%) was similar with cytological method (86.44%, P > 0.05); but its negative prediction value (93.56%) was lower than HPV test (97.06%, P < 0.05)., Conclusions: The positive rates of TERC gene amplification increased as cervical diseases worsened. TERC gene amplification is related to HPV infection. The gain of chromosome 3q26 in cytological specimens is an effective molecular genetic biomarker in screening of CINII or higher and invasive cervical cancer.

Objective: To study the amplification of human telomerase RNA component (hTERC) gene in the cervical exfoliated cells from natural population in Shenzhen and to explore its relationship with human pappiloma-virus (HPV) infection, abnormal cervical cytology and cervical intraepithelial neoplasia (CIN)., Methods: Three hundred and eighty-eight women, 30 - 59 year-old and having normal intelligence in a community of Shenzhen, were examined with liquid-based cytology. Human pappiloma-virus (HR-HPV) was tested by hybrid capture II (HC-II) and hTERC gene detection by fluorescence in situ hybridization (FISH). Patients with ASCUS and above lesion, and/or positive HR-HPV results and/or amplification of hTERC gene were examined by colposcopy, multiple biopsies of cervical quadrant and pathology., Results: The frequencies of CIN I, II, III, cervical cancer were 55 (14.18%), 4 (1.03%), 9 (2.32%) and 1 (0.26%) respectively, with the rate of hTERC gene amplification as 8.76%. There were significantly differences of hTERC amplification among the samples with different cytological and histological lesions as well with HPV infection (P < 0.01). (1) The positive rate of HPV infection was 17.01%; the positive rates of hTERC gene amplification were 19.70% in HPV positive and 6.52% in HPV negative samples and the results were significantly different (P < 0.01). (2) Cytologically, the rates of hTERC gene amplification appeared to be as follows: NILM (5.97%), ASCUS (18.75%), LSIL (10.00%), ASC-H (66.67%), HSIL (100.00%). There was a marked increase of hTERC amplification in patients with HSIL and above lesions (P < 0.01). (3) On histology findings, the rates of hTERC gene amplification were as follows: NILM (0%), CIN I (5.45%), CIN II (50.00%), CIN III (77.78%), and invasive carcinoma (100.00%). There was a marked increase of hTERC amplification in patients with CIN II and above lesions (P < 0.01)., Conclusion: There was a close correlation between amplification of hTERC and histological as well cytological lesions which increased progressively along with the severity of cytological and histological grade. The evidence of hTERC, with or without amplification, might serve as a prognostic indicator to measure the grade of lesion.

To evaluate genomic amplification of the human telomerase RNA gene (TERC) as a supportive approach to cytopathology or histopathology in diagnosis of low-grade and high-grade uterine cervical lesions, 1,033 Chinese women at three medical centers had liquid-based thin-layer cytopathologic examination and TERC detection by fluorescence in situ hybridization (FISH). Human papillomavirus DNA testing, colposcopy with or without biopsy, and histopathologic examination were conducted as needed. In cytopathologic examination, genomic amplification of TERC was found in 30 of 659 (4.6%) normal or benign cellular changes; in 23 of 170 (13.5%) atypical squamous cells of undetermined significance (ASCUS); in 8 of 28 (28.6%) atypical squamous cells with high-grade squamous intraepithelial lesion possible (ASC-H); and in 26 of 103 (25.2%) low-grade (LSIL) and 64 of 73 (87.7%) high-grade (HSIL) squamous intraepithelial lesions; with pairwise significant difference (P< 0.05) in each, except ASC-H and LSIL (chi(2) = 0.127, P = 0.72). In histopathologic examination, TERC was amplified in 28 of 671 (4.2%) normal, inflammatory, or wart cases; in 17 of 233 (7.3%) cervical intraepithelial neoplasia grade 1 cases (CIN 1); in 27 of 39 (69.2%) CIN 2 cases; in 57 of 67 (85.1%) CIN 3 cases; and in 22 of 23 (95.7%) cervical cancer cases; with pairwise significant difference in each (P < 0.05). The number of cells with abnormal signals increased and the abnormal signal patterns were diversified with increasing severity of cervical dysplasia. FISH detection of TERC amplification may provide an effective, noninvasive approach in conjunction with cytopathologic or histopathologic evaluation for differential diagnosis of low- and high-grade cervical disorders.