BMC Research Notes

ObjectivesRaw data of fetal measurements is often difficult to track down in the literature, and researchers are often limited to comparing their own original data to summary tables in synthetic volumes, or investing considerable time and resources into collecting this data themselves. Here, we present the R data package FetalBoneData, which we hope will improve access to such datasets. MethodsData was sourced from the literature (primarily Fazekas and Kosas (1978), which is long out of print) and work by the lead author. This was collated into a series of.csv files, before being put together into an R data package. ResultsWe apply the data in this package to compare the measurements of the humerus in a 19th Century fetal collection (Liverpool fetal collection, this paper) against that reported by Fazekas and Kosa (1978) as a case study of the utility of the package. DiscussionThe benefit of publishing such data in an open-source format, easily accessible through a popular statistical package, can significantly improve the availability of this type of data. It is hoped that data will be continuously added to the package, further improving its utilization.

BackgroundBreast cancer, though rare in females aged 10-24 years, typically presents as a more aggressive disease with poorer prognosis in this population due to distinct biological features and delayed presentation. Creating awareness and disseminating knowledge of breast cancer at a young age is crucial for future risk reduction. However, levels of awareness and knowledge of breast cancer with related factors among females aged 10-24 years in Sub-Saharan Africa are not well documented. This study maps the research landscape on awareness and knowledge of breast cancer, symptoms, signs, risk factors and screening methods and identifies gaps for further research and practice. MethodologyUsing the Arksey and OMalley framework for scoping studies, we reviewed literature published between 2010 and 2024. English-language articles were identified through systematic searches of PubMed, Google Scholar, and EBSCOhost. The search strategy employed a combination of keywords such as "knowledge," "awareness," "breast cancer," "risk factors," "symptoms," "signs," "breast self-examination," "clinical breast examination," "mammography," "10-24 year old females," and "Sub-Saharan African countries." ResultsA significant research gap was identified, with only 20 studies addressing breast cancer knowledge and awareness in this specific demographic and region. The majority of the identified studies were conducted in Nigeria. While some general awareness of breast cancer exists, detailed knowledge of specific symptoms, risk factors, and breast self-examination techniques is poor across many parts of Sub-Saharan Africa. The media was frequently cited as a major source of information. ConclusionResearch on breast cancer awareness, risk factors, and screening practices among females aged 10-24 in Sub-Saharan Africa is limited. The documented low levels of knowledge highlight a critical need for targeted and more effective public health interventions. Further studies are essential to investigate the underlying reasons for this knowledge gap and should be implemented across diverse settings within Sub-Saharan Africa. Author SummaryWe have reviewed how much is known about breast cancer awareness among young women aged 10-24 years in Sub-Saharan Africa, and we found a significant lack of research. While we know this group needs more knowledge about breast cancer and its risks to improve future outcomes, the available information is limited, with most studies focusing on Nigeria. Our review of existing literature shows that young women in Sub-Saharan Africa generally lack detailed knowledge of specific symptoms, risk factors, and breast self-examination techniques, even though media is a common source of general information. This gap highlights an urgent need for more targeted public health efforts and further studies to understand and address these issues more broadly across the region.

BackgroundArabidopsis thaliana sepals are excellent models for analyzing growth of entire organs due to their relatively small size, which can be captured at a cellular resolution under a confocal microscope [1]. To investigate how growth of different tissue layers generates unique organ morphologies, it is necessary to live-image deep into the tissue. However, imaging deep cell layers of the sepal is practically challenging, as it is hindered by the presence of extracellular air spaces between mesophyll cells, among other factors which causes optical aberrations. Image processing is also difficult due to the low signal-to-noise ratio of the deeper tissue layers, an issue mainly associated with live imaging datasets. Addressing some of these challenges, we provide an optimized methodology for live imaging sepals and subsequent image processing. This helps us track the growth of individual cells on the outer and inner epidermal layers, which are the key drivers of sepal morphogenesis. ResultsFor live imaging sepals across all tissue layers at early stages of development, we found that the use of a bright fluorescent membrane marker, coupled with increased laser intensity and an enhanced Z-resolution produces high-quality images suitable for downstream image processing. Our optimized parameters allowed us to image the bottommost cell layer of the sepal (inner epidermal layer) without compromising viability. We used a voxel removal technique to visualize the inner epidermal layer in MorphoGraphX [2, 3] image processing software. Finally, we describe the process of optimizing the parameters for creating a 2.5D mesh surface for the inner epidermis. This allowed segmentation and parent tracking of individual cells through multiple time points, despite the weak signal of the inner epidermal cells. ConclusionWe provide a robust pipeline for imaging and analyzing growth across inner and outer epidermal layers during early sepal development. Our approach can potentially be employed for analyzing growth of other internal cell layers of the sepals as well. For each of the steps, approaches, and parameters we used, we have provided in-depth explanations to help researchers understand the rationale and replicate our pipeline.

Background and ObjectiveQuestionnaires are essential tools in many scientific fields, including health and medicine. However, the analysis of paper-and-pencil questionnaires is time consuming, source of errors and expensive, limiting its use in large cohort studies. Computer-based questionnaires might be a valuable alternative but they may introduce bias, especially for sensitive questions, and they require programming skills. The aim of this study is to develop a reliable and adaptable open-source technique (i.e. LightQuest) to automatically analyse various types of scanned paper-and-pencil questionnaires with closed questions, including those with inverted scale. MethodsTo evaluate the usefulness of LightQuest, the time needed for 7 experimenters for manually code 10 sets of 4 frequently used questionnaires and the number of errors (i.e. reliability) were compared with the time and errors their made using LightQuest. ResultsLightQuest was twice as fast as the manual analysis, even though the time to create the reference model was taken into account (933s vs. 1935s, t(2)=8.81, p<0.001). Without model creation, the reduced analysis time was more pronounced, with an average of 2.77s.question-1 for the manual technique versus 0.55s.question-1 for LightQuest (t(2)=22.5, p<0.001). Moreover, during correction of the 5180 questions performed by the 7 experimenters, LightQuest made a total of 2 errors versus 46 with the manual technique (q(2)=4.53, p<0.05). ConclusionLightQuest demonstrated clear superiority both in terms of time and reliability. The script of this first open-source technique, which does not require programming skills, is downloadable in supplemental data and may become an asset for all studies using questionnaires.

BackgroundBreast cancer is the most prevalent cancer among women in India, characterized by late-stage diagnoses and high mortality rates. Existing breast cancer risk prediction models, such as the Gail and Tyrer-Cuzick models, were primarily developed using Western datasets, limiting their applicability to the Indian context due to socio-demographic, genetic, and cultural differences. ObjectiveThis pilot study aims to develop and validate a machine learning (ML)-based breast cancer risk prediction model tailored specifically to the Indian population, addressing the limitations of traditional tools, with the potential for future methodological expansion to build more robust and generalizable models. MethodsA retrospective case-control pilot study was conducted using data from the National Cancer Institute (NCI)-AIIMS, comprising 590 breast cancer cases and 1,366 controls. Data preprocessing included cleaning, missing value imputation, and feature engineering of 66 clinical, genetic, and lifestyle factors. To address class imbalance and multivariate complexities, the XGBoost ensemble model was employed. Model performance was evaluated using accuracy, recall, precision, F1-score, and AUC-ROC metrics. Gini index values were used to interpret model predictions and identify key features for risk stratification. ResultsThe model demonstrated robust predictive performance with an accuracy of 0.89 and AUC-ROC > 0.9, sensitivity of 73.95%, and specificity of 94.90% on the test dataset. Feature importance analysis enabled the development of a reduced model using the top 20 features, maintaining high accuracy and clinical relevance. The reduced model simplifies risk assessment in resource-limited settings. ConclusionsThis pilot study introduces a population-specific ML-based breast cancer risk prediction tool tailored to the Indian demographic. By incorporating culturally relevant variables and leveraging advanced machine learning techniques, the model addresses key limitations of Western-centric risk prediction tools. While the current methodology serves as an initial framework, it can be further expanded and refined to develop more robust and generalizable models for broader population coverage. Integration into clinical workflows and further validation across diverse Indian populations could transform early detection and personalized intervention strategies, significantly reducing the burden of breast cancer in India.

This study aimed at determining the extent to which methods for estimating trends in fertility without use of birth history could be used on Kenyan surveys data by employing the own-children method (OCM) and reverse survival (RS) method in estimating fertility trend in the country. The study used data from 2015/16 Kenya Integrated Household and Budget Survey (KIHBS) and 2014 Kenya Demographic and Health Survey (KDHS). Data evaluation was done in order to obtain optimal fertility estimates. 2015/16 KIHBS data reported a Whipples index of 49.0 and 57.5 for terminal digits 0 and 5 respectively. Myers blended index was 2.9 and this was an indication that in general the data was accurate and therefore did not require any adjustment to improve its quality before use. Results from 2015/16 KIHBS showed that RS estimated Total Fertility Rate to be 3.5 as compared to OCM that estimated it to be 3.8. The results from 2014 KDHS dataset were consistent when using both RS and OCM. The two indirect methods can give consistent fertility estimates when the reference period is closer to the survey period but in the fourth and fifth year RS tends to systematically overstate fertility as compared to OCM. This study found out that in the absence of full birth history data, RS and OCM can reliably estimate consistent fertility estimates and trend.

BackgroundMendelian randomization (MR) has emerged as a valuable tool for causal inference in genetic epidemiology. Existing MR methods have issues related to pleiotropy and offer limited comprehensiveness. Here, we introduce an integrated MR analysis pipeline designed for GWAS summary statistics data. Our pipeline integrates feature selection, harmonization, and checkpoint mechanisms to improve the accuracy and reliability of MR analysis. MethodsIn classical GWAS, the p-value threshold usually does not guarantee to identify causal single-nucleotide polymorphisms (SNPs). In such cases, t-statistics can be considered as imperative and robust criteria for identifying causal SNPs. Therefore, in this study, we computed the t-statistic for all independent SNPs remained after linkage disequilibrium pruning. Next, prior to harmonization, we removed SNPs having a t-statistic below the average t-statistic value. Furthermore, our pipeline incorporates sensitivity analysis tests at each step to reduce the chances of directional pleiotropy. Result and ConclusionWe applied our pipeline to single-sample and two-sample MR study designs, encompassing diverse populations and a wide range of diseases. Our results demonstrate superior performance compared to existing MR methods. In conclusion, our research presents an integrated MR analysis pipeline that significantly enhances the accuracy and reliability of MR studies. By outperforming existing methods and providing comprehensive validation, this pipeline represents a valuable tool for researchers in genetics and epidemiology.

BackgroundBreeding towards resilient rice varieties is often constrained by the limited data on root system architecture obtained from relevant agricultural environments. Knowledge on the genotypic differences and responses of root architecture to environmental factors is limited due the difficulty of analysing soil-grown rice roots. An improved method using imaging is thus needed, but the existing methods were never proven successful for rice. Here, we aimed to evaluate and improve a higher throughput method of image-based root phenotyping for rice grown under field conditions. Rice root systems from seven experiments were phenotyped based on the "shovelomics" method of root system excavation followed by manual root phenotyping and digital root analysis after root imaging. Analyzed traits were compared between manual and image-based root phenotyping systems using Spearman rank correlations to evaluate whether both methods similarly rank the phenotypes. For each trait, the relative phenotypic variation was calculated. A principal component analysis was then conducted to assess patterns in root architectural variation. ResultsSeveral manually collected and image-based root traits were identified as having a high potential of differentiating among contrasting phenotypes, while other traits are found to be inaccurate and thus unreliable for rice. The image-based traits projected area, root tip thickness, stem diameter, and root system depth successfully replace the manual determination of root characteristics, however attention should be paid to the lower accuracy of the image-based methodology, especially when working with older and larger root systems. ConclusionsThe challenges and opportunities of rice root phenotyping in field conditions are discussed for both methods. We therefore propose an integrated protocol adjusted to the complexity of the rice root structure combining image analysis in a water bath and the manual scoring of three traits (i.e. lateral density, secondary branching degree, and nodal root thickness at the root base). The proposed methodology ensures higher throughput and enhanced accuracy during root phenotyping of soil grown rice in fields or pots compared to manual scoring only, it is cheap to develop and operate, it is valid in remote environments, and it enables fast data extraction.

Study ObjectivesThe coding of semi-structured interview transcripts is a critical step for thematic analysis of qualitative data. However, the coding process is often labor-intensive and time-consuming. The emergence of generative artificial intelligence (GenAI) presents new opportunities to enhance the efficiency of qualitative coding. This study proposed a computational pipeline using GenAI to automatically extract themes from interview transcripts. MethodsUsing transcripts from interviews conducted with maternity care providers in South Carolina, we leveraged ChatGPT for inductive coding to generate codes from interview transcripts without a predetermined coding scheme. Structured prompts were designed to instruct ChatGPT to generate and summarize codes. The performance of GenAI was evaluated by comparing the AI-generated codes with those generated manually. ResultsGenAI demonstrated promise in detecting and summarizing codes from interview transcripts. ChatGPT exhibited an overall accuracy exceeding 80% in inductive coding. More impressively, GenAI reduced the time required for coding by 81%. DiscussionGenAI models are capable of efficiently processing language datasets and performing multi-level semantic identification. However, challenges such as inaccuracy, systematic biases, and privacy concerns must be acknowledged and addressed. Future research should focus on refining these models to enhance reliability and address inherent limitations associated with their application in qualitative research.

IntroductionBreast cancer is a global health concern and a leading cause of morbidity and mortality. Breast self examination (BSE) is a commonly recommended screening method for breast cancer. This study intends to assess the knowledge of breast cancer and its screening in women aged 18-25 in Bangalore city and to assess the knowledge, attitude and practice of women aged 18-25 in Bangalore city with regards to breast self examination among the female college students studying non medical courses in Bangalore City, Karnataka, India. Materials and methodsA questionnaire based study was conducted among women aged 18-25 in Bangalore city and their scores in the fields of knowledge, attitude, and practice were calculated.Independent sample t-test and one-way analysis of variance (ANOVA) test of significance were used to examine the relation between the demographic characteristics and scores. ResultsThe mean knowledge score was 3.05 (SD:1.37, range:0-6). Knowledge scores significantly differed across family income per month (P<0.05), age groups (P<0.001), and level of education of father (P<0.05). The mean attitude score was 3.95 (SD:1.78, range:1-8). The mean practice score was 0.83 (SD: 0.954, range:0-3).Practice scores varied significantly across age groups (P<0.05), level of education of mother (P<0.05) and occupation of father (P<0.001).The correlation coefficients are 0.484 and 0.257 respectively (p<0.05). ConclusionBreast Self Exam (BSE) is an easy method of early detection of breast cancer. There is a need to develop and adopt culturally appropriate and proven interventions to inform and train women about BSE in a diverse country like India. The study can be used as a basis to help raise awareness about BSE and its benefits among the same.

BackgroundCurrent form of genome-wide association studies (GWAS) is inadequate to accurately explain the genetics of complex traits due to the lack of sufficient statistical power. It explores each variant individually, but current studies show that multiple variants with varying effect sizes actually act in a concerted way to develop a complex disease. To address this issue, we have developed an algorithmic framework that can effectively solve the multi-locus problem in GWAS with a very high level of confidence. Our methodology consists of three novel algorithms based on graph theory and machine learning. It identifies a set of highly discriminating variants that are stable and robust with little (if any) spuriousness. Consequently, likely these variants should be able to interpret missing heritability of a convoluted disease as an entity. ResultsTo demonstrate the efficacy of our proposed algorithms, we have considered astigmatism case-control GWAS dataset. Astigmatism is a common eye condition that causes blurred vision because of an error in the shape of the cornea. The cause of astigmatism is not entirely known but a sizable inheritability is assumed. Clinical studies show that developmental disorders (such as, autism) and astigmatism co-occur in a statistically significant number of individuals. By performing classical GWAS analysis, we didnt find any genome-wide statistically significant variants. Conversely, we have identified a set of stable, robust, and highly predictive variants that can together explain the genetics of astigmatism. We have performed a set of biological enrichment analyses based on gene ontology (GO) terms, disease ontology (DO) terms, biological pathways, network of pathways, and so forth to manifest the accuracy and novelty of our findings. ConclusionsRigorous experimental evaluations show that our proposed methodology can solve GWAS multi-locus problem effectively and efficiently. It can identify signals from the GWAS dataset having small number of samples with a high level of accuracy. We believe that the proposed methodology based on graph theory and machine learning is the most comprehensive one compared to any other machine learning based tools in this domain.

Mammography is used as secondary prevention for breast cancer. Computer-aided detection and image-based short-term risk estimation were developed to improve the accuracy of mammography. However, most approaches inherently lack the ability to connect observations at the mammography level to observations of cancer onset and progression seen at a smaller scale, which can occur years before imageable cancer and lead to primary prevention. The Hurst exponent (H) can quantify mammographic tissue into regions of dense tissue undergoing active restructuring and regions that remain passive, with amounts of active and passive dense tissue that differ between cancer and controls at diagnosis. A longitudinal retrospective case-control study was conducted to test the hypothesis that differences can be detected before diagnosis and changes could signal developing cancer. Mammograms and reports were collected from 50 patients from Maine Medical Center in 2015 with at least a 5-year screening history. Age-matching patients within 2 years created a primary dataset, and within 5 years, a secondary dataset was created to test for sensitivity. The amount of passive (H [&ge;] 0.55) and active dense tissue (0.45 < H < 0.55) was calculated for each breast and was predicted by creating a linear mixed-effects model. Cancer status was a predictor for passive (p = 0.036) and active (p = 0.025) dense tissue using the primary dataset. However, when increasing the power, cancer status was a predictor for active dense tissue (p = 0.013), while breast status (p = 0.004), time (p = 0.009), and interaction (p = 0.038) were predictors for passive dense tissue. This suggests active dense tissue is a risk for cancer and passive dense tissue is an indication of developing cancer. Required Key MessagesO_LIMammographic dense breast tissue can be separated into regions of active and passive. C_LIO_LIThere is more active dense breast tissue in pathology-confirmed cancer cases than controls. C_LIO_LIIncreases in passive dense tissue in a breast could indicate a developing tumor. C_LI

BackgroundConsanguineous unions are defined as the matrimony between individuals who are blood relatives. Researchers in all over the world worked on this issue and they checked the ratio of prevalence and effects of consanguinity in different regions of world. This research was conducted in the District Faisalabad, upper Punjab. ObjectiveTo find rate of consanguinity, coefficient of inbreeding (F) and its impacts. MethodsThe data was collected from six tehsils of District Faisalabad by interviewing the subjects. The data collected within the time span of six months. Total of 2366 subjects were interviewed after their consent approval. ResultsThe rate of consanguinity was noted 41.83% with 0.03053 coefficient of inbreeding. High rate of consanguinity (23.36%) was noted among first cousins. The distantly related and not related unions were 35.64% and 22.56% respectively. The rate of consanguineous unions in six tehsils ranged from 33.99% in Jaranwala to 53.85% in Tandlianwala. Consanguineous marriages were noted high in Punjabi speaking subjects, in housewives, in reciprocal marital types, in grand-parents and one couple family types and Rajpoot castes. There was found no significant differences of consanguinity in rural and urban areas. The rate of still births was noted high (82.25%) in consanguineous unions while neonatal, post neonatal and child mortality was low such less as 6.45%, 8.06% and 3.22% respectively. The prenatal mortality was noted slightly high 44.94% in consanguineous unions as compared to non-consanguineous unions. The congenital malformation rate was 6.29% in all marital unions but this rate was high (59.06%) in consanguineous unions as compared to non-consanguineous unions (40.93%). This is a pilot study to analyze the potential of inbreeding coefficient (F) in the District Faisalabad.

BackgroundLymphedema is clinically manifested as swelling in the extremities due to abnormal accumulation of protein rich in the extravascular interstitial space resulting in irreversible structural changes to the limb (s). The aim of this explorative work was to evaluate the effect of Cyperus rotundus root (CRR) ethanolic extract in a mouse tail model of secondary lymphedema. Method: Mice were temporally anaesthetised under sterile condition and the skin from the tail was removed from distal part of the trunk after leaving 1cm of distance. The animals were divided into Experimental control (EC) and Cyperus rotundus root (CRR) 80 mg/kg b.w. /day) treated groups. Change in tail volume and circumference were monitored for 20 days. TNF, SOD and catalase were estimated from blood obtained through retro-orbital at day 0, 5, 10 and 15. Further TS of upper part of the tail skin was stained with H&E stain to document histological changes mRNA level of COX-2 was estimated from the skin. RESULTSEC group showed gradual rise in tail oedema post-surgery (PS), elevated inflammation, oxidative stress and histopathological alterations. However in CRR (80 mg/kg b.w./day) treated group shown the reduced tail oedema after post-surgery. TNF, SOD and catalase levels were significantly less in CRR then EC group supporting anti-inflammatory potential. CRR protected the tail from structural damage. It also down regulated the expression of COX-2 in comparison to EC group. Conclusions: CRR ethanolic extract significantly attenuated secondary lymphedema indicating it potential for therapeutic use. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=158 SRC="FIGDIR/small/558373v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@62d3b9org.highwire.dtl.DTLVardef@3a5053org.highwire.dtl.DTLVardef@6fae9aorg.highwire.dtl.DTLVardef@3027aa_HPS_FORMAT_FIGEXP M_FIG C_FIG

2.PurposeChildhood cancer survivors have an increased risk of long-term health complications, including treatment-related hearing loss. Although early detection is important, many adult survivors do not attend hearing screenings in clinical centers because visits can be logistically or emotionally burdensome. The HEAR study tested an alternative, community-based audiological screening option delivered in hearing aid shops in Switzerland. We evaluated its effectiveness, including clinical outcomes and survivor engagement, and developed a plan for potential implementation in clinical practice. MethodsWe invited childhood cancer survivors (CCS) registered in the Childhood Cancer Registry and diagnosed with cancer before age 21 years to a free pure-tone audiogram at hearing aid shops across Switzerland. Participants completed a baseline questionnaire before the hearing test, and two follow-up questionnaires evaluating feasibility and user experience. We gathered qualitative insights through semi structured interviews with participants and hearing aid shop employees, and group discussions with healthcare professionals. Interviews were analyzed using thematic analysis, and group discussions using template analysis. We evaluated the program according to the RE-AIM framework, incorporating both quantitative and qualitative data. ResultsOf 1604 invited CCS, 476 (30%) consented and 319 (20%) completed audiometric testing. The program identified clinically relevant hearing loss in 71 participants (22%) using the SIOP-Boston ototoxicity scale. Following the screening, five participants acquired hearing aids. Both CCS participants and clinicians were open to this alternative screening option and provided predominantly positive feedback. Together with clinicians, we developed an implementation plan that outlines how this screening option could be integrated into follow-up care. ConclusionThis simple and accessible community-based screening option could complement existing follow-up care, particularly for CCS who are no longer engaged in structured follow-up care.

ObjectivesIn clinical and epidemiological studies, the modified Poisson and least-squares regression analyses for binary outcomes have been used as standard multivariate analysis methods to provide risk ratio and risk difference estimates. However, their ordinary Wald-type confidence intervals can suffer from finite-sample biases in the robust variance estimators, and the coverage probabilities of true effect measures are substantially below the nominal level (usually 95%). To address this issue, new accurate inference methods are needed. MethodsWe propose two accurate inference methods based on the estimating equation theory for these regression models. A remarkable advantage of these regression models is that the correct models to be estimated are known, that is, conventional binomial regression models with log and identity links. Using this modeling information, we first derive the quasi-score statistics, whose robust variances are estimated using the correct model information, and then propose a confidence interval based on the regression coefficient test using{chi} 2 -approximation. To further improve the large sample approximation, we propose adapting a parametric bootstrap method to estimate the sample distribution of the quasi-score statistics using the correct model information. In addition, we developed an R package, rqlm (https://doi.org/10.32614/CRAN.package.rqlm), that can implement the new methods via simple commands. ResultsIn extensive simulation studies, the coverage probabilities of the two new methods clearly outperformed the ordinary Wald-type confidence interval when the regression function assumptions were correctly specified, especially in small and moderate sample settings. We also illustrated the proposed methods by applying them to an epidemiological study of epilepsy. The proposed methods provided wider confidence intervals, reflecting statistical uncertainty. ConclusionsThe current standard Wald-type confidence intervals may provide misleading evidence. Erroneous evidence can potentially influence clinical practice, public health, and policymaking. These possibly inaccurate results should be circumvented using effective statistical methods. These new inference methods would provide more accurate evidence for future medical studies.

BackgroundFFPE tissue samples are commonly used in biomedical research and are a valuable source for next-generation sequencing in oncology, however, extracting RNA from these samples can be difficult the quantity and quality achieved can impact the downstream analysis. This study compared the effectiveness of seven different commercially available RNA extraction kits specifically designed for use with FFPE samples in terms of the quantity and quality of RNA recovered. MethodsThis study used 9 samples of FFPE tissue from three different types of tissue (Tonsil, Appendix and lymph node of B-cell lymphoma) to evaluate RNA extraction methods. Three sections of 20m of each sample were combined per sample. The slices were distributed in a systematic manner to prevent any biases. Each of the 7 commercially available RNA extraction kits were used according to manufacturers instructions, with each sample being tested in triplicate resulting in a total of 189 extractions. The concentration, RNA integrity number (RIN) and DV200 of each extraction was analysed using a LabChip to determine the quantity and quality of the recovered RNA. ResultsThis study found that despite processing the FFPE samples in the same standardized way, there were disparities in the quantity and quality of RNA recovered across the different tissue types. Additionally, the study found notable differences in the quantity of RNA recovered when using different extraction kits. In terms of quality, three of the kits performed better than the other four in terms of RNA integrity number (RIN) and DV200 values. ConclusionThough many laboratories have developed their own protocols for specific tissue types, using commercially available kits is still a popular option. Although these kits use similar processes and extraction procedures, the amount and quality of RNA obtained can vary greatly between kits. In this study, among the kits tested, while the Roche kit, provided a nearly systematic better-quality recovery than other kits, the ReliaPrep FFPE Total RNA miniprep from Promega yielded the best ratio of both quantity and quality on the tested tissue samples.

BackgroundHuman papillomavirus (HPV) is one of the major infectious agents of cervical cancer. Papanicolaou (pap) smear study is generally carried out to screen the initial cervical condition and consequently specific PCR based study is carried out to recognize the different types of HPV. In the present study, we would like to screen the frequency of HPV infection in the women with normal and abnormal cervical discharges. MethodsIn our study, 216 subjects were recruited. Cervical cytology was done by Pap smear test. Nested PCR was carried out using MY09/11 and GP 5+/6+ primers to screen HPV infection. Result and conclusionA significant co-relation between HPV infection and early sexual intercourse was observed. We found a higher HPV prevalence in the age group below 29 years(35.48%). 85.71% SCC patients were positive for HPV infection, 80% HSIL patients were positive for HPV infection, 75% LSIL patients were positive for HPV infection; 66.7% ASCUS patients were positive for HPV infection. 50% ASC-H patients were positive for HPV infection. HPV positive was found in 22.22% of the subjects, among them 16.75% show normal cytology (NILM).

BackgroundIn acute myeloid leukemia studies, event-free survival (EFS) is defined as time until treatment failure, relapse, or death, whichever occurs first. Since 2020 and 2022, respectively, the US Food and Drug Administration and the European LeukemiaNet recommend analysing treatment failures as day-1 events. This data modification can lead to a potentially large drop in the estimated EFS at day 1. If censoring occurs, the Kaplan-Meier estimator obtained from the recoded data underestimates this drop. Our aim is to obtain an unbiased estimate for EFS as basis for further inference. MethodsWe define "event on day 1" as one event type and " event after day 1" as a competing event in the original data and use the Aalen-Johansen estimator of the cumulative incidence curve to estimate event-specific transition probabilities, which are combined in one EFS estimate. To analyse effects on day 1 treatment failure and other post-day-1 EFS events separately, a formal link to cure models is established by equating treatment failures with the "cured" proportion in cure model terminology. Additionally, a variance estimator, confidence intervals, confidence bands, and simultaneous testing procedures are derived. ResultsOur new estimation method differs from the Kaplan-Meier estimator in settings in which some treatment failures are censored, as in the interim analysis of the AMLSG 09-09 study. If almost no treatment failures are censored, the two estimation methods do not differ. The cure model and simultaneous testing are able to estimate effects on day 1 treatment failure and other post-day-1 EFS events separately and function independently of whether data is modified. ConclusionsThe Kaplan-Meier estimator evaluated on the recoded data underestimates the drop at day 1 if treatment failures are censored. With sufficient follow-up, this bias disappears, and results coincide with our novel approach.

BackgroundProjection of cases and deaths in an epidemic such as CoViD-19 is hazardous and the early projections were way-off the actual pattern. However, we now have actual data for more than 50 consecutive days in India that can be effectively used for projection. MethodsWe closely track the trend and use the same pattern for projection. We call this Empirical Model. We also fit a Theoretical Model based on a Gamma function on the pattern of some of the previous epidemics. ResultsThe Empirical Model predicts the peak around the fourth week of May and the near end of the epidemic by the end of June 2020. The maximum number of active cases is likely to be nearly 75,000 during the second week of June. This would mean a peak demand of nearly 15,000 beds and nearly 4000 ventilators. The case-fatality based on those who have reached an outcome was nearly 10% in the first week of May and is likely to remain at this level for some time. Theoretical Model projected a peak of nearly 2500 new cases per day in the second week of May that seems to have been already breached. This model predicts the near end of the epidemic by the middle of July 2020. ConclusionWith the current trend, the end of the epidemic is in sight with relatively mild consequences in India compared with most other countries.