UNASSIGNED: Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer\'s disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation.
UNASSIGNED: This review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation.
UNASSIGNED: A total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for white matter segmentation tasks in MICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods.
UNASSIGNED: Although various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research.

Science and society typically respond to dysmenorrhea-or painful menstrual cramps-as a normal, natural, and inevitable part of menstruation. This normalization has greatly contributed to the systemic dismissal of painful menstrual cramps. Stigma, secrecy, and the expectation to \"cope\" fuel the normalization of menstrual pain. In this article, I argue that the normalization of menstrual pain restricts the ability to share an excruciating menstrual pain in a way that would otherwise elicit alarm or concern. This can cause clinicians to downgrade menstrual pain, and even menstruating persons to downgrade their own pain. I refer to the dismissal of menstrual pain as an example of a pain-related motivational deficit. A pain-related motivational deficit describes instances in which an utterance fails to motivate due to societal practices and ideas that make it difficult to recognize the import of the embodied experience being shared.
“Just” a painful period: why we are not concerned by reported menstrual painIt is widely believed that painful menstrual cramps are just a normal part of the menstrual cycle; something that all menstruating persons are expected to deal with. There is also a stigma around periods and an expectation to keep the experience of periods hidden. This creates a process known as normalization. Because painful menstrual cramps are normalized, it is easier to dismiss patients who report painful menstrual cramps. In this article, I argue that the idea that painful menstrual cramps are normal makes it difficult for others to be concerned or alarmed by reports of menstrual pain. Reports of menstrual pain are downgraded or are seen as not that bad. When we are unable to see how bad a pain is because society believes the reported pain is normal, the pain report fails to elicit concern from the listener. I call this process a pain-related motivational deficit.

Background: Initial evaluation of the hypothalamus-pituitary-thyroid axis is done by measuring serum free thyroxine (fT4) and thyrotropin concentrations. For correct interpretation of these measurements, reliable age-specific reference intervals (RIs) are fundamental. Since neonatal fT4 RIs conforming to the Clinical and Laboratory Standards Institute guidelines are not available for all assays, we set out to create literature-based uniform age-specific neonatal fT4 RIs that may be used for every assay. Methods: For meta-analysis of individual participant fT4 concentrations, we systematically searched MEDLINE and Embase (search date December 6, 2023; PROSPERO registration CRD42016041871). We searched for studies reporting fT4 concentrations in healthy term newborns aged 2-27 days, born to mothers without thyroid disease in iodine-sufficient regions. Authors were invited to supply data. Due to standardization differences between assays, data could not be combined for meta-analysis directly, and we attempted to normalize the data using two distinct methods. Results: We obtained 4206 fT4 concentrations from 20 studies that used 13 different assays from 6 manufacturers. First, we set out to normalize fT4 data using the mean and standard deviation of (assay-specific) adult RIs. fT4 concentrations were transformed into Z-scores, assuming a normal distribution. Using a linear mixed-effects model (LMM), we still found a significant difference between fT4 concentration across studies (p < 0.001), after this normalization. As a second approach, we normalized the fT4 concentrations using data from a method/assay comparison study. We used the relationship between the Cobas assay and the other assays as a reference point to convert all values to Cobas values. However, this method also failed to produce consistent results, with significant differences between the normalized data (LMM p < 0.001). Conclusions: We conclude that our attempts at normalizing fT4 assay results were unsuccessful. Confounders related to our unsuccessful analysis may be assay related and/or biological. These findings have significant implications for patient care, since relying on RIs from literature may result in erroneous interpretation of results. Therefore, we strongly recommend to establish local RIs for accurate interpretation of serum fT4 concentrations in neonates.

Treatment of locally advanced oral cancer requires multidisciplinary care, including surgery, radiotherapy, and chemotherapy, which varies based on the stage of the disease, site of involvement, and surgical access. Oral cancer usually presents with an increased recurrence rate and potential for distant metastatic spread. It confers a poor prognosis with a 50% mortality rate after five years. Oral metronomic chemotherapy aims to achieve higher patient compliance due to its ease of administration, lower dosage, and lesser side effects than conventional IV regimens of platinum-based drugs. In this review, we have summarized the relevant literature to benefit the readers regarding the potential application of metronomic therapy in the management of oral cancer.

The human microbiome is an emerging research frontier due to its profound impacts on health. High-throughput microbiome sequencing enables studying microbial communities but suffers from analytical challenges. In particular, the lack of dedicated preprocessing methods to improve data quality impedes effective minimization of biases prior to downstream analysis. This review aims to address this gap by providing a comprehensive overview of preprocessing techniques relevant to microbiome research. We outline a typical workflow for microbiome data analysis. Preprocessing methods discussed include quality filtering, batch effect correction, imputation of missing values, normalization, and data transformation. We highlight strengths and limitations of each technique to serve as a practical guide for researchers and identify areas needing further methodological development. Establishing robust, standardized preprocessing will be essential for drawing valid biological conclusions from microbiome studies.

Goal: Artificial intelligence applied to medical image analysis has been extensively used to develop non-invasive diagnostic and prognostic signatures. However, these imaging biomarkers should be largely validated on multi-center datasets to prove their robustness before they can be introduced into clinical practice. The main challenge is represented by the great and unavoidable image variability which is usually addressed using different pre-processing techniques including spatial, intensity and feature normalization. The purpose of this study is to systematically summarize normalization methods and to evaluate their correlation with the radiomics model performances through meta-analyses. This review is carried out according to the PRISMA statement: 4777 papers were collected, but only 74 were included. Two meta-analyses were carried out according to two clinical aims: characterization and prediction of response. Findings of this review demonstrated that there are some commonly used normalization approaches, but not a commonly agreed pipeline that can allow to improve performance and to bridge the gap between bench and bedside.

The field of analytical chemistry has been significantly advanced by the availability of state-of-the-art instrumentation, allowing for the development of novel applications in this field. However, in many cases, the direct interpretation of the recorded data is often not straightforward, hence some level of pre-processing is required (e.g., baseline correction, derivatives, normalization, smoothing). These techniques have become a critical first step for the successful analysis of the data recorded, and it is recommended to use them before the application of chemometrics (e.g., classification, calibration development). The aim of this paper is to provide with an overview of the most used pre-processing methods applied to instrumental analytical methods (e.g., spectroscopy, chromatography). Examples of their application in near infrared and UV-VIS spectroscopy as well as in gas chromatography will be also discussed. Overall, this paper provides with a comprehensive understanding of pre-processing techniques in analytical chemistry, highlighting their importance during the analysis and interpretation of data, as well as during the development of accurate and reliable chemometric models.

BACKGROUND: As laws change and cannabis use increases, it is worthwhile to take a rich account of cannabis stigmas in society, and this review identifies a disjunction between quantitative investigations on cannabis users and qualitative investigations on the same population. This is also the first attempt to explicate cannabis stigmas as they manifest on multiple analytical levels. Following brief explanations of the normalization hypothesis and the concept of stigma, this review is organized between structural (macro) stigmas, social (meso) stigmas, and personal (micro) stigmas. Furthermore, since cannabis stigmas are similar to the stigmas faced by sexual minorities in that each is physically concealable, the two groups are compared here because the literature base is more extensive with the latter.
METHODS: This qualitative review synthesizes the body of empirical studies on both medical and nonmedical cannabis use with attention to stigma, stereotypes, and other social consequences. Studies considered for the review mostly come from the social sciences, particularly sociology. The information presented here is primarily drawn from peer-reviewed articles on cannabis users in the USA, though research from similar national contexts is cited as well.
RESULTS: This review suggests claims of normalization may be premature. While stigmas surrounding cannabis appear to have diminished, there is little evidence that such stigmas have entirely disappeared. It is possible that sweeping claims of cannabis normalization may be symptomatic of unchecked social privileges or social distance from cannabis users. Such claims may also be the product of valuing quantitative data over the nuanced accounts uncovered through qualitative investigations.
CONCLUSIONS: This substantial coverage of the literature indicates the lived experience of a post-prohibition society is not the same as a one where cannabis is normalized. Individuals working with those who use cannabis should not assume stigmas have disappeared, especially since cannabis stigmas often intersect with other sources of social inequality. While a comprehensive discussion of ways to combat lingering social stigmas is beyond the scope of this review, it concludes by highlighting some of the strategies identified through research which help users resist or mitigate these oppressive forces. Future research would be wise to prioritize the experiences of people of color, women, and adult populations if the hope is to identify ways to further normalize the plant in American society.

Dozens of normalization methods for correcting experimental variation and bias in high-throughput expression data have been developed during the last two decades. Up to 23 methods among them consider the skewness of expression data between sample states, which are even more than the conventional methods, such as loess and quantile. From the perspective of reference selection, we classified the normalization methods for skewed expression data into three categories, data-driven reference, foreign reference, and entire gene set. We separately introduced and summarized these normalization methods designed for gene expression data with global shift between compared conditions, including both microarray and RNA-seq, based on the reference selection strategies. To our best knowledge, this is the most comprehensive review of available preprocessing algorithms for the unbalanced transcriptome data. The anatomy and summarization of these methods shed light on the understanding and appropriate application of preprocessing methods.

The heterogeneity among previous studies of future mortality projections due to climate change has often hindered comparisons and syntheses of resulting impacts. To address this challenge, the present study introduced a novel method to normalize the results from projection studies according to different baseline and projection periods and climate scenarios, thereby facilitating comparison and synthesis. This study reviewed the 15 previous studies involving projected climate change-related mortality under Representative Concentration Pathways. To synthesize their results, we first reviewed the important study design elements that affected the reported results in previous studies. Then, we normalized the reported results by CO2 concentration in order to eliminate the effects of the baseline period, projection period, and climate scenario choices. For twenty-five locations worldwide, the normalized percentage changes in temperature-attributable mortality per 100 ppm increase in global CO2 concentrations ranged between 41.9% and 330%, whereas those of total mortality ranged between 0.3% and 4.8%. The normalization methods presented in this work will guide future studies to provide their results in a normalized format and facilitate research synthesis to reinforce our understanding on the risk of climate change.