BMC Research Notes

ObjectiveIn vitro cytokine release assays are essential tools in immunological research, yet their reliability can be influenced by various pre-analytical factors. This study was initiated to investigate the potential impact of lithium heparin whole blood collection tubes from different manufacturers on assay performance. The objective of this work is to urge the scientific community to consider whole blood collection tubes as a significant and often underestimated source of variability in cell-based assays. Results description210 whole blood samples from healthy donors were collected in plastic lithium heparin tubes sourced from 20 different suppliers worldwide. Testing revealed that 8 out of 20 of these tubes contained endotoxin levels >0.1{square}EU/mL, which triggered significant variability in cytokine responses (IFN-{gamma} and IL-6) in cell-based assays. A dose-response curve using LPS from E. coli confirmed the impact of endotoxins on interferon-{gamma} production.

BACKGROUNDThe performance of a functional annotation approach for RNA-seq bioinformatics pipelines was to be compared with the method where groups of genes are generated with no relation to ontologes. Three publicly available RNA-Seq experiments for Amphimedon queenslandica sponge were used for the designed comparison. One of these experiments was referred in the publication where stages of embryo development were compared for a wide range of animal species. METHODSThe expression levels were re-calculated here for three independent series of experiments. The functional annotation of differential expression levels was than conducted. This allow to compare an applicability of the two approaches, and to re-evaluate the interpretation provided in the mentioned publication. RESULTSIt was confirmed by the conventional approach that Wnt and Notch pathways do operate in a development of a sponge embryo. The method of annotation which uses unbounded grouping of genes was effective in an ability to separate development stages of sponge embryo. In addition, the published results were by a suggestion distorted by an artifact, caused by a positive feedback in the stage of data processing.

BackgroundIndia has the most number of COVID-19 cases in the world currently, second only to the USA. The COVID-19 pandemic has caused high mortality not only in patients but also health care providers. In this paper, our aim is to analyze the cases of deaths in Indian doctors due to COVID-19 infection. MethodsThe details of data were taken from the list of the deceased doctors in India due to COVID-19 infection, which was compiled by the Indian Medical Association (IMA), the top body of Indian doctors practicing modern allopathic medicine. The key data fields of age, specialty, and geographical location of the deceased doctors were extracted from the given list, and analysis was performed. ResultsA total of 382 COVID-related deaths and 2174 infections were reported amongst doctors in India till 10 September 2020, with a case fatality rate of 16.7% among Indian doctors, which was ten times the CFR of 1.7% in the general population. Among the practicing doctors, after excluding the resident doctors and house surgeons, the CFR was 36.4%, which is almost 22 times more than what was seen in general population of India. The average age of COVID-related deaths in Indian doctors was 60.8 years, with a median age of 60; 62% of deaths among doctors were in the above 60 years, age group. The maximum number of deceased doctors were amongst general practitioners 225(58.9%). Among the specialists, most deaths were seen in paediatricians 26(6.8%), medical specialists 24(6.3%), general surgeons 22(5.8%), obstetricians & gynecologists 16(4.2%), and anesthesiologists 14(3.7%). The highest COVID-19 related deaths in doctors were seen in the Indian states of Tamil Nadu, Karnataka, Andhra Pradesh, Gujarat and Maharashtra in that order. ConclusionsThe mortality rate is very high among doctors in India compared to the general population. The average age of COVID-19 related death was 60 years among doctors. General practitioners and 60 years+ doctors are at a much higher risk of mortality among the doctors. The states with the high number of COVID-19 cases in India, also had a higher number of doctor deaths.

BackgroundTraditional, complementary, and integrative medicine (TCIM) encompasses a wide range of healthcare practices and is of growing global interest. Preprints, scientific manuscripts posted prior to formal peer review, offer opportunities to address challenges in TCIM research, including limited funding, publication delays, and concerns about methodological quality. By increasing the visibility and speed of research dissemination, preprints may help strengthen the TCIM evidence base. This bibliometric analysis examined the characteristics of TCIM-related preprints posted on servers with TCIM subject filters. MethodsPreprints were sourced from the ASAPbio preprint server directory, limited to servers with TCIM-related categories. Data extraction (June 4, 2024 to January 14, 2025) included title, DOI, abstract, authors, affiliations, preprint posted date, publication journal (if applicable), publication date, preprint type, keywords, number of versions, citations, comments, and funding information. Author geographic distribution, collaboration networks, and key research topics were also analyzed. ResultsBetween 2012 and 2024, 1,980 TCIM preprints were posted across 11 servers. Research Square hosted the most preprints, and China contributed the highest number. Among the 612 preprints later published in journals, BMC Trials was the most common destination, with a median time of 4.89 months from preprint to publication. Funding information was often missing, but when reported, the National Natural Science Foundation of China was the most frequent sponsor. Overall, citation and comment activity was low. Wellcome Open Research had the highest average citations and comments per preprint among all servers. ConclusionThis study provides the first in-depth analysis of TCIM preprints, revealing active research areas and important gaps in preprint usage, geographical representation, and post-publication engagement. Findings highlight opportunities to improve transparency and research dissemination in TCIM through more consistent preprint practices and tracking.

Patient Simulators 1.0 is a novel display interface that provides an alternative means of showing real-time changes in physiological variables derived from human patient simulators. It is designed for teachers and learners of the sciences for whom showing variables on simulated clinical monitors may be less relevant. It allows import of comma separated data sets, and their display using customisable gauges and trendlines. Data from variables not normally shown on simulated patient monitors, e.g. pH, and data from variables that are not produced during the simulation but can be derived, e.g. stroke volume, can be displayed. It also allows presentation of data produced during simulation sessions carried out elsewhere without the need for proprietary simulation software. This interface may assist in wider adoption of the use of simulated data in teaching the basic science underpinning health and disease.

BackgroundThe act of diagnosis is one which precipitates semiotic closure, the complex integration of signs and symptoms through cognitive perspectives to ultimately activate causal reasoning and calibrate the assignment of a disease entity to the patient. In writing about this act, physicians encode both structured and unstructured information into the medical record. Unstructured information contains a latent structure which entwines both the cognitive components of the diagnostic act and the linguistic patterns associated with clinical documentation. Existing models of clinical language primarily use a physical or dialogic model of information as their basis, and do not adequately account for the complexity inherent in the diagnostic act. MethodsFraming the diagnostic information collected in clinical care as a narrative, we developed a model representative of said information, accounting for its content and structure, as well as the inherent complexity therein. Using an exemplar text, we present the use of known predication and semantic relations from ontological (the Unified Medical Language System) and linguistic theory (Rhetorical Structure Theory) to facilitate the operationalization of the model, and analyze the result. ResultsThe resulting model is demonstrated to be complex, representative of the clinical narrative text, and is fundamentally aligned with the clinical acts of both documentation and diagnosis. We find the models representation of the cognitive aspects of narrative consistent with models of reading, as well as an adequate model of information as presented by clinical medicine and the clinical sub-language. ConclusionsWe present a model to represent diagnostic information in the physicians note which accounts for the clinical and textual narrative precipitated by the cognition involved in encoding said information into the unstructured medical record. This model prepends the development of (computational) linguistic models of the clinical sublanguage within the physicians note as it relates to diagnosis, beyond the information level of the lexical unit. Such analysis would facilitate better reflection on the structure and meaning of the clinical note, offering improvements to medical education and care.

The sperm selection assay (SSA) is a method based on chemotaxis to obtain spermatozoa at the optimum physiological state to successfully fertilize the egg. It consists of a device made of acrylic and an attractant solution which includes progesterone. We evaluate potential cytotoxicity interactions by means of Neutral Red uptake, the MTT and colony formation assays, according to ISO normative. Here we showed that even stressing the conditions of the assays, the SSA device alone or together with the progesterone solutions employed, showed to be innocuous for the cells. Suggesting that SSA could be incorporated into the ART procedures.

Back groundTissue differentiation and organogenesis are exclusively embryonic-phase activities, and these two activities are strictly under the control of the embryonic proteome, the developing embryo is a prime source of molecules for regenerative therapy. Many ex-vivo rudimentary organoid culture studies confirmed the inherent biological function of embryonic factors. Before using embryonic proteins for in vivo therapies, their safety should be confirmed first. Hence, we aimed for an in vivo study to inject rat embryonic proteome extract (EPE) through the intravenous route and investigate the impact on immunological, biochemical, and hematological parameters in the adult rats. MethodsIn this study, we isolated rat embryonic proteins from the 14th, 16th, and 19th embryonic days by homogenization of embryos and isolated protein extracts through ultra-centrifugation. Six pairs of rats have been taken; six are allotted for control, and six are for the embryonic extract injection. The isolated embryonic protein extract was injected intravenously into the treatment group of rats and the normal saline into the control group. After the injections, blood samples are collected from both the treated and control groups to analyse immunological markers Il6, CRP, biochemical parameters creatinine, urea, sugar, proteins, albumin, globulin, and hematological parameters total leucocyte count, neutrophils, and lymphocyte percentage ResultAfter the EPE injection, biochemical parameters, immunological markers, and hematological parameters were analyzed in both control and treated groups. All the above mentioned parameters are within normal limits. Statistical analyses were done using the t-test for unequal variance (p< 0.05). We observed no significant difference between the control and treated groups, so in vivo injections of embryonic protein extract is safe for the use of in vivo regenerative therapies. ConclusionDespite extensive in vitro studies confirmed the biological function of embryonic growth factors for organ differentiations and rudimentary organoids, but in vivo clinical applications are not yet started because of immunogenicity, heterogeneity, and tumorigeniety. In our study we injected EPE intravenously and showed that EPE is non-immunogenic, non-heterogeneic, and non-tumorigenic. This study concludes that EPE is safe for in vivo injection, so that further studies can be continued for intra-organ injections for organ regenerative therapy.

Background. Journal clubs (JCs) are a popular education format. Interest in studying their impact is high, and authors often report positive results related to subjective parameters. Objective assessments of effectiveness are limited and contradictory. In this paper, we share our experience and describe our journal club effectiveness. Methods. We conducted a prospective cohort study within our online journal club. Meetings followed a discussion-based format and were held via Zoom, with timing and topics determined by voting in the club Telegram chat. Enrolment occurred in waves and included an application, entry test, and interview. During each recruitment wave, both club members (treatment group) and applicants (control) completed an admission test assessing knowledge of evidence-based medicine and statistics. Results. The JC currently comprises 27 members. Over the past year, 76 meetings were held, with 75% of participants grading their experience with 9 or 10 on a ten-point scale. Multivariate analysis demonstrated non-significantly results (SMD = 0.19 (95% CI 0.004; 0.38), p = 0.046) among participants. However, in other adjusted models, differences between groups were not statistically significant (p > 0.05). Conclusion. While the analysis of the subjective outcomes is consistent with findings from previous studies, the objective outcomes remain inconclusive. Further research is needed to refine the methodology for the organization and evaluation of journal clubs.

Studying for medical exams is a great undertaking traditionally considered a "rite of passage", as the depth and breadth of knowledge required increases progressively from undergraduate-, to postgraduate-, to fellowship-level exams. A common theme of these exams is the necessity of learning and committing to memory a large body of work, corroborating multiple disparate primary sources. The article presents new software, written to assist exam candidates with the process of knowledge acquisition and exam preparation. The software is written as an open-source plugin of the popular free note-taking software Obsidian and provides tight integration with the open-source reference management software Zotero. We then present a study system termed "Deep Sit" which incorporates a simple heading-based organization of medical knowledge: (Definition; Epidemiology; Etiology; Pathophysiology; Symptoms & Signs; Investigations; Treatment). We demonstrate how the Deep Sit study system is significantly facilitated through a walkthrough of these software components.

Cutaneotrichosporon oleaginosus is an oleaginous yeast with a high content of fatty acids and can accumulate more than 40% of its weight in lipids. It can grow on a wide range of carbon sources and side streams such as crude glycerol. The genome sequence of C. oleaginosus ATCC 20509 is reported here to contribute to its development as a biotechnological platform for producing microbial oils.

IntroductionUnravelling the health effects of multiple pollutants presents scientific and computational challenges. CorEx is an unsupervised learning algorithm that can efficiently discover multiple latent factors in highly multivariate datasets. Here, we used the CorEx algorithm to perform a hypothesis free analysis of demographic, biochemical, and toxic metal biomarker data. MethodsOur data included 77 variables from 2,750 adult participants of the National Health and Nutrition Examination Survey (NHANES 2015-2016). We used an implementation of the CorEx algorithm designed to deal with the features of bioinformatic datasets including mixed data-types. Models were fit for a range of possible latent variables and the best fit model was selected as that which resulted in the largest Total Correlation (TC) after adjustment for the number of parameters. Successive layers of CorEx were run to discovered hierarchical data structure. ResultsThe CorEx algorithm identified 20 variable clusters at the first layer. For the majority clusters, the associations between variables were consistent with known associations - e.g. gender and the hormones, estradiol and testosterone were included in the first cluster; blood organic mercury and blood total mercury were grouped in cluster 4, and cluster 6 included the liver function enzymes ALT, AST and GGT. At the second layer, 3 branches of were identified reflecting hierarchical structure. The first branch included numerous physiological biomarkers and several exogenous biomarkers. The second branch included a number endogenous and exogenous variables previously associated with hypertension, while the third branch included mercury biomarkers and some related endogenous biomarkers. DiscussionWe have demonstrated the CorEx algorithm as a useful tool for hypothesis free exploration of a biomedical dataset. This work extends previous implementations of CorEx by allowing mixed data-types to be modelled and the results showed that CorEx detected meaningful hierarchical structure. CorEx may facilitate exploration of novel datasets in future.

Medical education has promoted the rapid interpretation of the arterial blood gas (ABG) as a crucial skill in the management of critically ill patients. The first step of many algorithms focuses on using log-linear estimates to determine the internal consistency of ABG values. However, because of modern computing advances, these estimates are no longer necessary. We demonstrate an alternative method for ascertaining internal consistency of ABG values based on direct calculation derived directly from Henderson-Hasselbach and demonstrate its efficiency and accuracy. Deidentified ABG values were collected from electronic medical records for the month of September 2022. Estimated hydrogen concentration was calculated by log linear estimation and by the proposed method of direct calculation. Time of computation, a measure of efficiency, was also recorded. Of the 1008 ABGs used for analysis, 185 (18.4%) had estimated hydrogen concentrations that were greater than a 5% threshold according to the guidelines method, suggesting potential internal inconsistency. However, only 13 (1.3%) had estimated hydrogen concentrations greater than 5% by direct calculation. Analysis was also performed at a 10% threshold. On further analysis, 4 ABGs that were identified by the guidelines method as being internally inconsistent, but not identified by the direct calculation method, were found to have pH <7.0, outside the range provided for the current guidelines for log-linear estimation, and were thus incorrectly identified by the guidelines method as being internally inconsistent. The time of computation for direct calculation was approximately 8-fold faster, although the total time of calculation for this dataset was minimal. This study demonstrates a direct calculation method as an alternative approach to traditional guidelines to assess internal ABG value consistency, the first step of ABG interpretation. We demonstrate that direct calculation is more accurate, identifying less potentially internally inconsistent values, notably at extremes in pH. In addition, the direct calculation algorithm is 8-fold more efficient than guidelines-based algorithm, although actual processing times were miniscule for the data set regardless of the method used. Furthermore, with modern calculators and computers, the direct calculation method is easier to understand and implement than the guideline based approach. Therefore, based on the results of this study, we propose the use of direct calculation as an alternative to log-linear based estimation for the assessment of internal consistency of ABGs.

The management and organization of a large collection of academic documents is an important part of scientific research. This study explores the use of ChatGPT, a large language model from OpenAI, to extract insights from a large collection of academic documents stored in Mendeley. The study found that ChatGPT can be used to generate insightful graphs, concise summaries, and other tasks tailored to user needs. The study also demonstrated that ChatGPT can be used to analyze a large number of publications in PDF format. This suggests that ChatGPT could be a valuable tool for researchers who want to save time and effort by automating the analysis of their data. The GitHub repository for the source code and the output of this study is available at: https://github.com/MohamedAbuella/Analysis_Mendeley.

While Bacterial Artificial Chromosomes were once a key resource for the genomic community, they have been obviated, for sequencing purposes, by long-read technologies. Such libraries may now serve as a valuable resource for manipulating and assembling large genomic constructs. To enhance accessibility and comparison, we have developed a BAC restriction map database.

ObjectiveThe optimal timing of umbilical cord clamping in neonatal care has been a subject of debate for decades. Recently, artificial intelligence (AI) has emerged as a significant tool for providing medical information. This study aimed to compare the accuracy, reliability, and comprehensiveness of information provided by ChatGPT and Google regarding the effects of cord clamping timing in neonatal care. MethodsA comparative analysis was conducted using ChatGPT-4 and Google Search. The search terms included "cord clamping time," "early clamping," "delayed clamping," and "cord milking." The first 20 frequently asked questions (FAQs) and their responses from both platforms were recorded and categorized according to the Rothwell classification system. The accuracy and reliability of the answers were assessed using content analysis and statistical comparison. ResultsChatGPT outperformed Google in terms of scientific accuracy, objectivity, and source reliability. ChatGPT provided a higher proportion of responses based on academic and medical sources, particularly in the categories of technical details (40%) and delayed cord clamping benefits (30%). In contrast, Google yielded more information in early cord clamping effects (25%) and cord milking (20%). ChatGPT achieved 80% accuracy in medical information, whereas Google reached only 40%. ConclusionWhile both platforms offer valuable information, ChatGPT demonstrated superior accuracy and reliability in neonatal care topics, making it a more suitable tool for healthcare professionals. However, Google remains useful for general information searches. Future studies should explore AIs potential in clinical decision-support systems.

This study analyzes 794,811 digitized medical examina- tions from Indian life-insurance applicants, a working-age, urban-skewed demographic often undersampled by national surveys. The cohort exhibits a pronounced South-Asian car- diometabolic risk profile: among valid adult records, 41.9% met the criteria for dyslipidemia (driven heavily by low HDL and elevated triglycerides), and 61.4% met AHA 2017 crite- ria for stage 1 hypertension. However, canonicalizing this dataset across 33,244 diagnostic centers revealed significant heterogeneity in laboratory reference ranges. At the clinical prediabetes threshold of 110 mg/dL for fasting blood sugar, the record-pair disagreement rate across laboratories was 49.7%, with similar variance across other common tests. This structural inconsistency materially affects patient classi- fication and the tracking of disease prevalence, underscoring a critical need for the national standardization of laboratory reporting in India

In in-vitro or in-vivo bioassays, the no observed effect concentration (NOEC) is often determined. This simple procedure has several disadvantages, including the limitation of being able to estimate only experimental doses. Linear interpolation between adjacent doses overcomes this drawback while maintaining the level of a familywise error rate (FWER) using multiple contrast tests.

Teaching medical statistics for students of human medicine should include both theoretical content as well as its implementation in practice using statistical software. The training period with a software may be time-consuming individually, so there is a risk that imparting methodological skills comes up short in such courses. The flipped classroom concept aims at students to familiarize themselves with both the statistical software and the theoretical content in an individual learning phase prior to the course. Then, within an attendance course deepening both aspects under the lecturers supervision is possible. Availability of the software as well as a variety of material to prepare the course are essential presumptions for the flipped classroom to be successful. We present the concept and implementation of an online flipped classroom course using the software SAS Studio. Students used their own devices (laptops or tablets) during the self-learning and examination phases, respectively. The pure online implementation in the flipped classroom setting enabled to contribute to an equalisation of the study plan. Moreover, even more students had the opportunity to participate in this practically oriented course in medical statistics. Initial evaluation results demonstrated that the course is well-accepted among students, while the students performance level was similar when compared to a non-flipped teaching approach.

Nutritional epidemiology studies aim to investigate the impact of diet on disease risk, and their findings inform subsequent dietary recommendations. The issue of whether foods or nutrients should be the unit of exposure in such studies is a topic of debate. However, although nutrient-based analyses can elucidate biological mechanisms, they are technically problematic; furthermore, although foods have been considered the most suitable unit of dietary exposure because "we eat food, not nutrients" and the findings of food-based analyses can be easily translated into dietary advice, there is a greater risk of confounding and/or reverse causation because food selection and consumption are closely related to socio-economic and behavioural factors. This note describes an integrated approach that combines the positive aspects of nutrient-and food-based analyses. Individual dietary exposure is characterised by its overall macro-and micronutrient compositions and, in line with the compositional nature of dietary data, methods based on log-ratio transformations of nutrient compositions are used in diet/outcome association analyses that provide fully adjusted isocaloric estimates of the impact of specific nutrient balances on health outcomes. These estimates can then be combined with the nutrient compositions of selected foods to produce outcome-related scores which, as they are defined regardless of a subjects food selection/consumption, are not subject to confounding or reverse causation, and therefore have important implications for public health professionals. Data coming from an Italian population-based study were used to illustrate the approach in the context of diet/serum uric acid relationships, and some resulting new insights were briefly discussed.