Enhancing Data Science and Genomics Capacity of a Historically Black Medical College Through Interdisciplinary Training and Research Collaborations.

As data grows exponentially across diverse fields, effectively leveraging big data has become increasingly crucial. In data science and computational genomics, however, minority groups, including African Americans, are significantly underrepresented, coupled with the lack of resources and infrastructure in minority-serving institutions. This paper summarizes the second phase of our funded project that aims to enhance the data science capacity of Meharry Medical College (MMC), a Historically Black College/University (HBCU), by providing training and fostering collaborations between data scientists and researchers in basic science and biomedical fields.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.