Depression in Female Adolescents with Heavy Menstrual Bleeding.

OBJECTIVE: To assess the degree to which heavy menstrual bleeding is associated with depression, independent of hormonal contraception. STUDY DESIGN: We performed a retrospective cohort study of 1168 female adolescents 9-18 years old presenting to general pediatricians for heavy menstrual bleeding or well visits. Depression was the primary outcome and defined as a diagnosis in the health record. Univariable and multivariable regression models were fit to the data to identify factors associated with depression diagnosis. RESULTS: In total, 581 adolescents with heavy menstrual bleeding and 587 without heavy menstrual bleeding were included. Depression diagnoses occurred with greater frequency in youth with heavy menstrual bleeding compared with those without heavy menstrual bleeding (50.9% vs 24.2% P < .001; risk ratio 1.67, 95% CI 1.39-2.01) but did not significantly differ between those taking vs not taking hormonal contraception (risk ratio 0.99; 95% CI 0.84-1.17). Most patients with depression and heavy menstrual bleeding developed depression following or concurrent with heavy menstrual bleeding (261/296, 88%). Of these, 199 of 261 (76%) were treated with hormonal contraception, but the majority (168/199; 84%) were diagnosed with depression before initiation. CONCLUSIONS: Heavy menstrual bleeding is associated with depression diagnosis in female adolescents. The use of hormonal contraception was not associated with depression diagnosis in multivariable analysis, covarying heavy menstrual bleeding, age, body mass index, anxiety, sexual activity, and substance use. As hormonal contraception is often used to treat heavy menstrual bleeding, heavy menstrual bleeding may be partially driving previous reports of increased depression risk in those taking hormonal contraception.

A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT).

Machine learning techniques are widely used nowadays in the healthcare domain for the diagnosis, prognosis, and treatment of diseases. These techniques have applications in the field of hematopoietic cell transplantation (HCT), which is a potentially curative therapy for hematological malignancies. Herein, a systematic review of the application of machine learning (ML) techniques in the HCT setting was conducted. We examined the type of data streams included, specific ML techniques used, and type of clinical outcomes measured. A systematic review of English articles using PubMed, Scopus, Web of Science, and IEEE Xplore databases was performed. Search terms included "hematopoietic cell transplantation (HCT)," "autologous HCT," "allogeneic HCT," "machine learning," and "artificial intelligence." Only full-text studies reported between January 2015 and July 2020 were included. Data were extracted by two authors using predefined data fields. Following PRISMA guidelines, a total of 242 studies were identified, of which 27 studies met the inclusion criteria. These studies were sub-categorized into three broad topics and the type of ML techniques used included ensemble learning (63%), regression (44%), Bayesian learning (30%), and support vector machine (30%). The majority of studies examined models to predict HCT outcomes (e.g., survival, relapse, graft-versus-host disease). Clinical and genetic data were the most commonly used predictors in the modeling process. Overall, this review provided a systematic review of ML techniques applied in the context of HCT. The evidence is not sufficiently robust to determine the optimal ML technique to use in the HCT setting and/or what minimal data variables are required.

Dexmedetomidine provides optimum conditions during awake fiberoptic intubation in simulated cervical spine injury patients.

BACKGROUND AND AIMS: We undertook this study to assess if a small-dose of dexmedetomidine (DEX) for conscious sedation during awake fiberoptic intubation (AFOI) in simulated cervical spine injury (CSI) patients provides optimum conditions and fulfills the need of postintubation neurological examination required in such patients. The aim was to assess the efficacy of DEX on arousability and patient's comfort during AFOI in simulated CSI patients. MATERIAL AND METHODS: In this prospective, randomized double-blind study, 100 American Society of Anesthesiologists Grade I-II patients aged between 18 and 65 years scheduled for elective surgery under general anesthesia underwent AFOI under conscious sedation with DEX. After locally anesthetizing the airway and applying a cervical collar, patients either received DEX 1 µg/kg over 10 min followed by 0.7 µg/kg/h maintenance infusion or normal saline in the same dose and rate during AFOI. Targeted sedation (Ramsay sedation score [RSS] ≥2) during AFOI was maintained with midazolam [MDZ] in both groups. Statistical Analysis was performed using unpaired Student's t-test, Chi-square test, Mann-Whitney test and Wilcoxon-w test. RESULTS: The total number of patients requiring MDZ and the mean dose of MDZ required to achieve targeted sedation (RSS ≥2) was significantly less in DEX group compared to the placebo group (P < 0.001). Similarly, patient satisfaction score, heart rate, systolic, diastolic and mean arterial pressure and respiratory parameters were significantly better in DEX group (P < 0.001). Postintubation arousability in the two groups was comparable (P = 0.29). CONCLUSIONS: Dexmedetomidine provides optimum sedation without compromising airway or hemodynamic instability with better patient tolerance and satisfaction for AFOI. It also preserves patient arousability for the postintubation neurological assessment.

Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care.

BACKGROUND: Wearable sensors are increasingly being explored in health care, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consistency of data collected from wearable sensors. Moreover, preprocessing pipelines to clean, transform, normalize, and standardize raw data have not yet been fully optimized. OBJECTIVE: This study aims to conduct a scoping review of preprocessing techniques used on raw wearable sensor data in cancer care, specifically focusing on methods implemented to ensure their readiness for artificial intelligence and machine learning (AI/ML) applications. We sought to understand the current landscape of approaches for handling issues, such as noise, missing values, normalization or standardization, and transformation, as well as techniques for extracting meaningful features from raw sensor outputs and converting them into usable formats for subsequent AI/ML analysis. METHODS: We systematically searched IEEE Xplore, PubMed, Embase, and Scopus to identify potentially relevant studies for this review. The eligibility criteria included (1) mobile health and wearable sensor studies in cancer, (2) written and published in English, (3) published between January 2018 and December 2023, (4) full text available rather than abstracts, and (5) original studies published in peer-reviewed journals or conferences. RESULTS: The initial search yielded 2147 articles, of which 20 (0.93%) met the inclusion criteria. Three major categories of preprocessing techniques were identified: data transformation (used in 12/20, 60% of selected studies), data normalization and standardization (used in 8/20, 40% of the selected studies), and data cleaning (used in 8/20, 40% of the selected studies). Transformation methods aimed to convert raw data into more informative formats for analysis, such as by segmenting sensor streams or extracting statistical features. Normalization and standardization techniques usually normalize the range of features to improve comparability and model convergence. Cleaning methods focused on enhancing data reliability by handling artifacts like missing values, outliers, and inconsistencies. CONCLUSIONS: While wearable sensors are gaining traction in cancer care, realizing their full potential hinges on the ability to reliably translate raw outputs into high-quality data suitable for AI/ML applications. This review found that researchers are using various preprocessing techniques to address this challenge, but there remains a lack of standardized best practices. Our findings suggest a pressing need to develop and adopt uniform data quality and preprocessing workflows of wearable sensor data that can support the breadth of cancer research and varied patient populations. Given the diverse preprocessing techniques identified in the literature, there is an urgency for a framework that can guide researchers and clinicians in preparing wearable sensor data for AI/ML applications. For the scoping review as well as our research, we propose a general framework for preprocessing wearable sensor data, designed to be adaptable across different disease settings, moving beyond cancer care.

A Case Study of Enhancing the Data Science Capacity of an RCMI Program at a Historically Black Medical College.

As data grows exponentially across diverse fields, the ability to effectively leverage big data has become increasingly crucial. In the field of data science, however, minority groups, including African Americans, are significantly underrepresented. With the strategic role of minority-serving institutions to enhance diversity in the data science workforce and apply data science to health disparities, the National Institute for Minority Health Disparities (NIMHD) provided funding in September 2021 to six Research Centers in Minority Institutions (RCMI) to improve their data science capacity and foster collaborations with data scientists. Meharry Medical College (MMC), a historically Black College/University (HBCU), was among the six awardees. This paper summarizes the NIMHD-funded efforts at MMC, which include offering mini-grants to collaborative research groups, surveys to understand the needs of the community to guide project implementation, and data science training to enhance the data analytics skills of the RCMI investigators, staff, medical residents, and graduate students. This study is innovative as it addressed the urgent need to enhance the data science capacity of the RCMI program at MMC, build a diverse data science workforce, and develop collaborations between the RCMI and MMC's newly established School of Applied Computational Science. This paper presents the progress of this NIMHD-funded project, which clearly shows its positive impact on the local community.

Evaluating mobile Health technology use among cancer caregivers in the digital era.

Introduction: Digital health technology-based interventions have the potential to support cancer caregivers in caregiving responsibilities and in managing their own health and well-being. The objective of this study was to examine the association between caregiving characteristics and different types of digital health technologies used in a national sample of caregivers of patients undergoing hematopoietic cell transplantation (HCT). Methods: We conducted an online, cross-sectional survey of 948 HCT caregivers. Results: Spousal caregivers comprised nearly one-third of respondents (27.1%) with a median age of 59 years (range: 18-80 years), compared with parents (32.9%: 38 years), adult children (28.9%: 38 years), and other (11.1%; e.g. friend, other family member: 36 years). Almost two-thirds (65.4%) of all respondents reported using an app for fitness or step counting and 41.3% reported using a smartwatch. However, spousal caregivers were the least likely group to use mobile apps (0.72; P < 0.005) or smartwatches (OR = 0.46; P < 0.005) compared with parent caregivers in models adjusted for demographics and coping style. Caregiving for six months or greater was associated with the use of fewer apps compared with caregiving for less than six months in adjusted models (OR = 0.80, P < 0.005). Caregivers of patients receiving an allogeneic transplant (i.e. non-self-donor) used more apps on average than caregivers of patients receiving an autologous transplant (i.e. self-donor) in adjusted models (OR = 1.36, P < 0.005). Conclusion: Digital health technologies reflect promising avenues for supporting cancer caregivers. While digital technologies are becoming increasingly pervasive, older caregivers remain an underserved population. Future research should integrate older adult caregivers in the co-design and development activities of technology-driven caregiver support products.

Examining data visualization pitfalls in scientific publications.

Data visualization blends art and science to convey stories from data via graphical representations. Considering different problems, applications, requirements, and design goals, it is challenging to combine these two components at their full force. While the art component involves creating visually appealing and easily interpreted graphics for users, the science component requires accurate representations of a large amount of input data. With a lack of the science component, visualization cannot serve its role of creating correct representations of the actual data, thus leading to wrong perception, interpretation, and decision. It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers. To address common pitfalls in graphical representations, this paper focuses on identifying and understanding the root causes of misinformation in graphical representations. We reviewed the misleading data visualization examples in the scientific publications collected from indexing databases and then projected them onto the fundamental units of visual communication such as color, shape, size, and spatial orientation. Moreover, a text mining technique was applied to extract practical insights from common visualization pitfalls. Cochran's Q test and McNemar's test were conducted to examine if there is any difference in the proportions of common errors among color, shape, size, and spatial orientation. The findings showed that the pie chart is the most misused graphical representation, and size is the most critical issue. It was also observed that there were statistically significant differences in the proportion of errors among color, shape, size, and spatial orientation.

Electronic Health Record Portal Use by Family Caregivers of Patients Undergoing Hematopoietic Cell Transplantation: United States National Survey Study.

BACKGROUND: As family caregivers of patients undergoing hematopoietic cell transplantation have multifaceted caregiving responsibilities (such as medical, household, financial) of long duration, they also have multiple physical, social, psychological, and informational needs. OBJECTIVE: This study explored the prevalence of electronic health record patient portal use by family caregivers for managing both their own and their hematopoietic cell transplantation care recipient's health, as well as potential factors associated with portal use. METHODS: An electronic caregiver health survey, first developed via cognitive interviewing methods of hematopoietic cell transplantation caregivers, was distributed nationally (in the United States) by patient advocacy organizations to family caregivers of hematopoietic cell transplantation patients. It was used to assess self-reported caregiver demographics, caregiving characteristics, depression and anxiety with the Patient Health Questionnaire-4, coping with the Brief COPE, and caregiver portal use to manage care recipient's and their own health. RESULTS: We found that 77% of respondents (720/937) accessed electronic health record patient portals for their care recipients, themselves, or both. Multivariate models indicated use of care recipient electronic health record portals by caregivers was more likely with young, White, married, low-income caregivers caring for a parent, residing with the care recipient, and experiencing more caregiver depression. Caregiver use of their own electronic health record portal was more likely with young, White, high-income caregivers caring for a parent and experiencing chronic medical conditions of their own. Partially due to multicollinearity, anxiety and coping did not contribute independently to this model. CONCLUSIONS: Findings from the survey could open avenues for future research into caregiver use of technology for informational support or intervention, including wearables and mobile health. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/4918.

Evaluating health technology engagement among family caregivers of patients undergoing hematopoietic cell transplantation.

Purpose: Digital health technology-based interventions have the potential to support caregivers in their caregiving responsibilities and in managing their own health and well-being. Designing digital health technologies to support caregivers of patients undergoing hematopoietic cell transplantation requires evaluating their engagement with these technologies. The objective of this study was to examine the association between caregiving characteristics and different types of digital health technologies used. Methods: We conducted an online cross-sectional, national survey of 948 unpaid family caregivers of patients undergoing hematopoietic cell transplantation. Results: Almost two-thirds (65.4%) of respondents reported using an app for fitness or step counting, while 41.3% reported using a smartwatch. The average number of apps used was 3.3 (range 0-9). In adjusted models, adult children who were caregivers (OR=5.82, p<0.005) and caregivers of another relative (OR=2.51, p<0.005) were significantly more likely to use a fitness tracker than caregivers of a child. Caregiving for six months or greater was associated with use of fewer apps compared with caregiving for less than six months in adjusted models (OR=0.80, p<0.005). Caregivers of patients receiving an allogeneic transplant used more apps on average than caregivers of patients receiving an autologous transplant, in adjusted (OR=1.36, p<0.005) models. Conclusion: Digital health technologies may reflect promising avenues for supporting caregivers of patients undergoing HCT. The rapid insurgence of telehealth, propelled by the current COVID-19 pandemic, emphasizes the need for a better understanding of digital health technology for future study design.

Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation.

Generalized structured component analysis (GSCA) is a theoretically well-founded approach to component-based structural equation modeling (SEM). This approach utilizes the bootstrap method to estimate the confidence intervals of its parameter estimates without recourse to distributional assumptions, such as multivariate normality. It currently provides the bootstrap percentile confidence intervals only. Recently, the potential usefulness of the bias-corrected and accelerated bootstrap (BCa) confidence intervals (CIs) over the percentile method has attracted attention for another component-based SEM approach-partial least squares path modeling. Thus, in this study, we implemented the BCa CI method into GSCA and conducted a rigorous simulation to evaluate the performance of three bootstrap CI methods, including percentile, BCa, and Student's t methods, in terms of coverage and balance. We found that the percentile method produced CIs closer to the desired level of coverage than the other methods, while the BCa method was less prone to imbalance than the other two methods. Study findings and implications are discussed, as well as limitations and directions for future research.

Delivery of molecules to cancer cells using liposomes from bacterial cultures.

Liposomes have a variety of applications as model systems to study enclosed biological membranes, as delivery vehicles for a variety of drugs and as micro- and nano-reactors, amongst others. However, preparation of liposomes requires use of expensive raw material (synthetic lipids) from specialized commercial suppliers, and ability to make reproducible preparations remains a specialized art till date. In this work, we prepared liposomes using natural lipids extracted from the bacteria Escherichia coli (E. coli), which are extremely economical compared to the synthetic lipids. We demonstrate robust procedures for convenient and reproducible preparations of 200-300 nm diameter liposomes from bacterial cells. We also show a potential application of these bacterial liposomes in delivery of aqueous molecules to cancer cells. We show not only intracellular uptake, but also biodegradation of the liposomes inside cancer cells. Our economical liposomes promise to serve as excellent model systems for studies on encapsulation of molecules inside soft materials with desired efficiencies. Additionally, they certainly show a strong potential to be tools for research in diverse areas ranging from drug delivery applications to sub-micron reaction engineering for carrying out and understanding the mechanisms of chemical reactions in small enclosed volumes.

Gel-based nonradioactive single-strand conformational polymorphism and mutation detection: limitations and solutions.

Single-strand conformation polymorphism (SSCP) for screening mutations/single-nucleotide polymorphisms (SNPs) is a simple, cost-effective technique, saving an expensive exercise of sequencing each and every PCR reaction product and assisting in choosing only the amplicons of interest with expected mutation. The principle of detection of small changes in DNA sequences is based on the changes in single-strand DNA conformations. The changes in electrophoretic mobility that SSCP detects are sequence-dependent. The limitations faced in SSCP range from the routine polyacrylamide gel electrophoresis (PAGE) problems to the problems of resolving mutant DNA bands. Both these problems could be solved by controlling PAGE conditions and by varying physical and environmental conditions such as pH, temperature, voltage, gel type and percentage, addition of additives or denaturants, and others. Despite much upgrading of the technology for mutation detection, SSCP continues to remain the method of choice to analyze mutations and SNPs in order to understand genomic variations, spontaneous and induced, and the genetic basis of diseases.

Gel-based nonradioactive single-strand conformational polymorphism and mutation detection: limitations and solutions.

Single-strand conformation polymorphism (SSCP) for screening mutations/single-nucleotide polymorphisms (SNPs) is a simple, cost-effective technique, saving an expensive exercise of sequencing each and every polymerase chain reaction product and assisting in choosing only the amplicons of interest with expected mutations. The principle of detection of small changes in DNA sequences is based on changes in single-strand DNA conformations. The changes in electrophoretic mobility that SSCP detects are sequence dependent. The limitations faced in SSCP range from routine polyacrylamide gel electrophoresis (PAGE) problems to the problems of resolving mutant DNA bands. Both these problems can be solved by controlling PAGE conditions and by varying physical and environmental conditions such as pH, temperature, voltage, gel type and percentage, addition of additives or denaturants, and others. Despite much upgrading of the technology for mutation detection, SSCP remains the method of choice to analyze mutations and SNPs in order to understand genomic variations, both spontaneous and induced, and the genetic basis of diseases.

Microsatellite instability: an indirect assay to detect defects in the cellular mismatch repair machinery.

The DNA mismatch repair (MMR) pathway plays a prominent role in the correction of errors made during DNA replication and genetic recombination and in the repair of small deletions and loops in DNA. Mismatched nucleotides can occur by replication errors, damage to nucleotide precursors, damage to DNA, or during heteroduplex formation between two homologous DNA molecules in the process of genetic recombination. Defects in MMR can precipitate instability in simple sequence repeats (SSRs), also referred to as microsatellite instability (MSI), which appears to be important in certain types of cancers, both spontaneous and hereditary. Variations in the highly polymorphic alleles of specific microsatellite repeats can be identified using PCR with primers derived from the unique flanking sequences. These PCR products are analyzed on denaturing polyacrylamide gels to resolve differences in allele sizes of >2 bp. Although (CA)n repeats are the most abundant class among dinucleotide SSRs, trinucleotide and tetranucleotide repeats are also frequent. These polymorphic repeats have the advantage of producing band patterns that are easy to analyze and can be used as an indication of a possible MMR defect in a cell. The presumed association between such allelic variation and an MMR defect should be confirmed by molecular analysis of the structure and/or expression of MMR genes.

Functional IFNG polymorphism in intron 1 in association with an increased risk to promote sporadic breast cancer.

Interferon (IFN)-gamma is an important Th1 cytokine, which plays a role in immune surveillance and anti-tumor activity. A case-control study involving 54 sporadic breast cancer patients and 144 healthy controls was carried out to explore if the genotype variation of a proposed non-specific enhancer element with a dinucleotide (CA)n repeat in intron 1 has a role in the susceptibility to promote sporadic breast cancer. Genotype analysis carried out by single-strand length polymorphism and confirmed by sequencing showed an increased frequency of (CA)12 allele (P<0.001) and decreased frequencies of (CA)15 (P<0.01) and (CA)>15 (p<0.001) alleles in sporadic breast cancer patients as compared to controls. Further, in vitro reporter assays for (CA)12 and (CA)15 alleles suggested these to be associated with decreased and increased expressions, respectively, suggesting the (CA)12/(CA)12 background to act as one of the factors that could lead to low production of IFN-gamma. The study concludes that such genetic background for a proposed non-specific enhancer element with (CA)n repeat within intron 1 of the IFNG gene might put the individuals with this genotype at higher risk to promote the development of sporadic breast cancer due to a resultant compromised immune surveillance.