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The World Health Organization recommends human papillomavirus (HPV) testing for cervical screening. Extended genotyping can identify the highest‐risk HPV‐positive women. An inexpensive, rapid, mobile isothermal amplification assay (ScreenFire HPV RS test) was recently redesigned to yield four channels ordered by cancer risk (ie, hierarchical approach): HPV16, HPV18/45, HPV31/33/35/52/58 and HPV39/51/56/59/68. Stored specimens from 2076 women (mean age 30.9) enrolled in a colposcopy clinic, with high HPV prevalence, were tested with ScreenFire. We calculated hierarchical channel positivity and non‐hierarchical channel and type positivity, according to histologic diagnosis (256 cancer, 350 cervical intraepithelial neoplasia [CIN]3, 409 CIN2, 1020 < CIN2) and known virologic reference results (Linear Array and TypeSeq). Additionally, we analyzed ScreenFire time‐to‐positive up to 60 min by channel and histology. Overall clinical sensitivity for CIN3+ was 94.7% (95% confidence interval 92.6‐96.4), similar to Linear Array (92.3, 89.7‐94.3) and TypeSeq (96.0, 93.9‐97.6). Sensitivity was high for all types and channels. The hierarchical approach was well in line with HPV typing and histologic diagnosis, prioritizing higher risk women having HPV16 and precancer. For HPV16, time‐to‐positive was shorter in women with precancer. ScreenFire showed excellent agreement with research reference typing tests and detection of CIN2+. Risk‐based type results could help guide clinical management of HPV‐positive women. Time‐to‐positive combined with genotyping might be useful. ScreenFire is rapid, mobile, relatively inexpensive and designed for implementation of HPV‐based screening and management, including in lower‐resource settings. Further validation in screening by self‐sampling and practical effectiveness merit evaluation. [ABSTRACT FROM AUTHOR]

There is limited access to effective cervical cancer screening programs in many resource‐limited settings, resulting in continued high cervical cancer burden. Human papillomavirus (HPV) testing is increasingly recognized to be the preferable primary screening approach if affordable due to superior long‐term reassurance when negative and adaptability to self‐sampling. Visual inspection with acetic acid (VIA) is an inexpensive but subjective and inaccurate method widely used in resource‐limited settings, either for primary screening or for triage of HPV‐positive individuals. A deep learning (DL)‐based automated visual evaluation (AVE) of cervical images has been developed to help improve the accuracy and reproducibility of VIA as assistive technology. However, like any new clinical technology, rigorous evaluation and proof of clinical effectiveness are required before AVE is implemented widely. In the current article, we outline essential clinical and technical considerations involved in building a validated DL‐based AVE tool for broad use as a clinical test. [ABSTRACT FROM AUTHOR]

Background: India shares the largest burden of under-nutrition in world. The aim of this study was to conduct follow-up assessment of under-nourished children attending anganwadi center (AWC). Methods: This was a retrospective cohort study conducted in 50 AWC of Tapi district. Total 529 children aged 1-6 years diagnosed as under-nourished before 1 year were included. Pre-structured questionnaire was used for present day assessment of these children followed by Epi-info mediated analysis. Results: Children of literate mothers had higher weight gain. Ninety percent of children attended anganwadi regularly, however 25% of children dropped out for more than 1 month. In 1 year, growth had faltered in 20% children and was stagnant in 63% of them. Children who were treated for under-nutrition; that completed course at Child Development and Nutrition Center; and whose parents were counseled about the under-nourished status of child had higher weight gain than their counterparts. Conclusions: Parents of under-nourished children must be counseled about the nutritional status of their child. In cases of under-nourished child, referral to higher center must be ensured by health worker. Supplementary feeding as a long-term solution to country's under-nutrition problem should be studied in detail with the alternative solutions. [ABSTRACT FROM AUTHOR]

BACKGROUND: As smoking is a major risk factor in India, the objective of present study was to assess smoking patterns, expenditure and awareness amongst smokers and to examine factors associated with the severity of smoking in Surat city. METHODS: Community-based cross-sectional study was conducted with the use of pre-structured questionnaire tool targeting 281 current smokers in the slums of 20 Urban Health Centers. Smokers were categorized based on pack-years, which means the number of packs smoked per day multiplied by the duration of smoking (mild <5, moderate 5-15, and severe >15), and based on the number of cigarette/bidis smoked per day without the duration component (mild <10, moderate 10-19, and severe ≥20). Factors associated with severity of smoking were examined within two approaches with help of Epi-info mediated analysis. RESULTS: Lower severity of smoking was associated with smaller number of family members who smoke (p<0.001). Groups of mild and moderate-severe smokers were similar with regards to education, socioeconomic class, and awareness regarding hazards of smoking (p>0.05). With the influence of friends (50%), about 60% smokers engage into this habit before the age of 20 years. Though only 20% of current smokers were not aware of the consequences of active smoking, more than 50% did not know about the same for passive smoking. The lowest socioeconomic class spends 44% of their income on tobacco products compared to 7% in the highest class. CONCLUSION: The study provides insights for information, education and counseling (IEC) activi- ties which should take into account health impact of bidi smoking, low awareness of health impact of passive smoking, and higher percentage of total monthly expenditure on tobacco among low-in-come household resulting in crowding out of ex- penditures on other needs. [ABSTRACT FROM AUTHOR]

Deepening understanding of cervical cancer pathogenesis has yielded one-dose prophylactic human papillomavirus (HPV) vaccines and accurate HPV-based cervical screening tests. Knowing the heterogeneous carcinogenic potential of the individual high-risk HPV types permits prioritization of vaccination and screening strategies. However, "correct" (i.e., safe and effective) treatment of women found to have precancer is still undefined, forcing reliance on one or more rounds of untargeted destructive/excisional treatment. Both over-treatment and under-treatment are common results. Until safe and effective anti-HPV therapies are invented, defining optimal destructive/excisional treatment of precancer remains a fundamental and under-researched challenge, especially in resource-constrained settings. See related article by King et al., p. 681., (©2023 American Association for Cancer Research.)

Visual inspection of the cervix with acetic acid (VIA), though error prone, has long been used for screening women and to guide management for cervical cancer. The automated visual evaluation (AVE) technique, in which deep learning is used to predict precancer based on a digital image of the acetowhitened cervix, has demonstrated its promise as a low-cost method to improve on human performance. However, there are several challenges in moving AVE beyond proof-of-concept and deploying it as a practical adjunct tool in visual screening. One of them is making AVE robust across images captured using different devices. We propose a new deep learning based clustering approach to investigate whether the images taken by three different devices (a common smartphone, a custom smartphone-based handheld device for cervical imaging, and a clinical colposcope equipped with SLR digital camera-based imaging capability) can be well distinguished from each other with respect to the visual appearance/content within their cervix regions. We argue that disparity in visual appearance of a cervix across devices could be a significant confounding factor in training and generalizing AVE performance. Our method consists of four components: cervix region detection, feature extraction, feature encoding, and clustering. Multiple experiments are conducted to demonstrate the effectiveness of each component and compare alternative methods in each component. Our proposed method achieves high clustering accuracy (97%) and significantly outperforms several representative deep clustering methods on our dataset. The high clustering performance indicates the images taken from these three devices are different with respect to visual appearance. Our results and analysis establish a need for developing a method that minimizes such variance among the images acquired from different devices. It also recognizes the need for large number of training images from different sources for robust device-independent AVE performance worldwide.