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PubMedDate not supplied

Development and implementation of a smoking cessation clinic in community pharmacy practice.

OBJECTIVE: To describe a pharmacist-operated program to help people stop smoking. SETTING: Community chain (mass-merchandise) pharmacy practice sites in Virginia. PARTICIPANTS: Faculty at the Virginia Commonwealth University School of Pharmacy (VCU) and 15 practicing pharmacists. PRACTICE DESCRIPTION: Smoking cessation clinics within the pharmacy departments of seven Target stores. PRACTICE INNOVATION: With input from the practicing pharmacists and using the most current strategies reported in the literature, faculty at VCU developed a training manual for smoking cessation counseling. This manual was used to facilitate a 4-hour training session and also served as a resource for pharmacists in the smoking cessation clinics. Pharmacists were taught about behavioral modifications, the Transtheoretical Model of Change as applied to smoking cessation, documentation, the Fagerström Test for Nicotine Dependence, smoking cessation therapies, patient counseling techniques, physical assessment skills, and how to develop an individualized action plan for smoking cessation. As pharmacists established smoking cessation clinics, issues such as workflow, continuous quality improvement, and marketing were addressed by the faculty members and practitioners. RESULTS: Using the process and tools described in this article, pharmacists successfully established and operated smoking cessation clinics. CONCLUSION: The process and materials developed for this demonstration project can serve as templates for other pharmacy faculty members and community pharmacists who wish to offer smoking cessation clinics.

Community Pharmacy Services↗

PubMedDate not supplied

Kaiser Permanente Colorado Region Pharmacy Department: innovative leader in pharmacy practice.

OBJECTIVE: To describe innovative programs within the Kaiser Permanente Pharmacy Department in the Colorado Region. SETTING: One of eight regions of the nation's largest nonprofit health maintenance organization. PRACTICE DESCRIPTION: The pharmacy department comprises two services, Clinical Pharmacy Services and Pharmacy Operations/Support Services, which are integrated to provide comprehensive care. PRACTICE INNOVATION: Within Clinical Pharmacy Services, the Primary Care Clinical Pharmacy Services team works alongside physicians to provide integrated patient care. The Centralized Clinical Pharmacy Service teams manage large groups of patients effectively and efficiently. The Clinical Pharmacy Specialty staff and the Disease State Management clinical pharmacy specialists provide focused drug therapy expertise. MAIN OUTCOME MEASURES: Clinical and economic outcomes; recognition by national organizations. RESULTS: Technological innovations are used within Pharmacy Operations/Support Services to increase pharmacists' time for patient care activities. The use of technology by the Pharmacy Automated Refill Center and the implementation of ScriptPro in the medical office pharmacies decrease dispensing demands by processing large volumes of prescriptions. Workflow in the medical office pharmacies has also been reengineered to increase efficiency. Various programs have been developed by the Pharmacy Information Technology Service to support patient care initiatives. Benchmark clinical and economic outcomes have been demonstrated. Positive outcomes have also resulted in quality and safety awards and captured the attention of national pharmacy and medical organizations. CONCLUSION: The Colorado Region Pharmacy Department has been recognized as a leader in pharmacy practice through the development of innovative services that provide exceptional patient care.

Anticoagulants↗

PubMedDate not supplied

Why we don't come: patient perceptions on no-shows.

PURPOSE: Patients who schedule clinic appointments and fail to keep them have a negative impact on the workflow of a clinic in many ways. This study was conducted to identify the reasons patients in an urban family practice setting give for not keeping scheduled appointments. METHODS: Semistructured interviews were conducted with 34 adult patients coming to the clinic for outpatient care. Interviews were audiotaped and transcribed verbatim. A multidisciplinary team used an immersion-crystallization organizing style to analyze the content of the qualitative interviews individually and in team meetings. RESULTS: Participants identified 3 types of issues related to missing appointments without notifying the clinic staff: emotions, perceived disrespect, and not understanding the scheduling system. Although they discussed logistical issues of appointment keeping, participants did not identify these issues as key reasons for nonattendance. Appointment making among these participants was driven by immediate symptoms and a desire for self-care. At the same time, many of these participants experienced anticipatory fear and anxiety about both procedures and bad news. Participants did not feel obligated to keep a scheduled appointment in part because they felt disrespected by the health care system. The effect of this feeling was compounded by participants' lack of understanding of the scheduling system. CONCLUSIONS: The results of this study suggest that reducing no-show rates among patients who sometimes attend might be addressed by reviewing waiting times and participants' perspectives of personal respect.

Adult↗

PubMed2025

MEANtools integrates multi-omics data to identify metabolites and predict biosynthetic pathways.

During evolution, plants have developed the ability to produce a vast array of specialized metabolites, which play crucial roles in helping plants adapt to different environmental niches. However, their biosynthetic pathways remain largely elusive. In the past decades, increasing numbers of plant biosynthetic pathways have been elucidated based on approaches utilizing genomics, transcriptomics, and metabolomics. These efforts, however, are limited by the fact that they typically adopt a target-based approach, requiring prior knowledge. Here, we present MEANtools, a systematic and unsupervised computational integrative omics workflow to predict candidate metabolic pathways de novo by leveraging knowledge of general reaction rules and metabolic structures stored in public databases. In our approach, possible connections between metabolites and transcripts that show correlated abundance across samples are identified using reaction rules linked to the transcript-encoded enzyme families. MEANtools thus assesses whether these reactions can connect transcript-correlated mass features within a candidate metabolic pathway. We validate MEANtools using a paired transcriptomic-metabolomic dataset recently generated to reconstruct the falcarindiol biosynthetic pathway in tomato. MEANtools correctly anticipated five out of seven steps of the characterized pathway and also identified other candidate pathways involved in specialized metabolism, which demonstrates its potential for hypothesis generation. Altogether, MEANtools represents a significant advancement to integrate multi-omics data for the elucidation of biochemical pathways in plants and beyond.

Metabolomics↗

PubMed2025

RBC-GEM: A genome-scale metabolic model for systems biology of the human red blood cell.

Advancements with cost-effective, high-throughput omics technologies have had a transformative effect on both fundamental and translational research in the medical sciences. These advancements have facilitated a departure from the traditional view of human red blood cells (RBCs) as mere carriers of hemoglobin, devoid of significant biological complexity. Over the past decade, proteomic analyses have identified a growing number of different proteins present within RBCs, enabling systems biology analysis of their physiological functions. Here, we introduce RBC-GEM, one of the most comprehensive, curated genome-scale metabolic reconstructions of a specific human cell type to-date. It was developed through meta-analysis of proteomic data from 29 studies published over the past two decades resulting in an RBC proteome composed of more than 4,600 distinct proteins. Through workflow-guided manual curation, we have compiled the metabolic reactions carried out by this proteome to form a genome-scale metabolic model (GEM) of the RBC. RBC-GEM is hosted on a version-controlled GitHub repository, ensuring adherence to the standardized protocols for metabolic reconstruction quality control and data stewardship principles. RBC-GEM represents a metabolic network is a consisting of 820 genes encoding proteins acting on 1,685 unique metabolites through 2,723 biochemical reactions: a 740% size expansion over its predecessor. We demonstrated the utility of RBC-GEM by creating context-specific proteome-constrained models derived from proteomic data of stored RBCs for 616 blood donors, and classified reactions based on their simulated abundance dependence. This reconstruction as an up-to-date curated GEM can be used for contextualization of data and for the construction of a computational whole-cell models of the human RBC.

Humans↗

PubMed2025

Expanding and improving analyses of nucleotide recoding RNA-seq experiments with the EZbakR suite.

Nucleotide recoding RNA sequencing methods (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.) are powerful approaches for assaying transcript population dynamics. In addition, these methods have been extended to probe a host of regulated steps in the RNA life cycle. Current bioinformatic tools significantly constrain analyses of NR-seq data. To address this limitation, we developed EZbakR (https://github.com/isaacvock/EZbakR), an R package to facilitate a more comprehensive set of NR-seq analyses, and fastq2EZbakR (https://github.com/isaacvock/fastq2EZbakR), a Snakemake pipeline for flexible preprocessing of NR-seq datasets, collectively referred to as the EZbakR suite. Together, these tools generalize many aspects of the NR-seq analysis workflow. The fastq2EZbakR pipeline can assign reads to a diverse set of genomic features (e.g., genes, exons, splice junctions), and EZbakR can perform analyses on any combination of these features. EZbakR extends standard NR-seq mutational modeling to support multi-label analyses (e.g., s4U and s6G dual labeling), and implements an improved hierarchical model to better account for transcript-to-transcript variance in metabolic label incorporation. EZbakR also generalizes dynamical systems modeling of NR-seq data to support analyses of premature mRNA processing and flow between subcellular compartments. Finally, EZbakR implements flexible and well-powered comparative analyses of all estimated parameters via design matrix-specified generalized linear modeling. The EZbakR suite will thus allow researchers to make full, effective use of NR-seq data.

Software↗

PubMed2025

OmnibusX: A unified platform for accessible multi-omics analysis.

OmnibusX is an integrated, privacy-centric platform that enables code-free multi-omics data analysis by bridging computational methodologies with user-friendly interfaces. Designed to overcome challenges posed by fragmented analytical tools and high computational barriers, OmnibusX consolidates workflows for diverse technologies - including bulk RNA-seq, single-cell RNA-seq, single-cell ATAC-seq, and spatial transcriptomics - into a single, cohesive application. The application integrates established open-source tools such as Scanpy, DESeq2, SciPy, and scikit-learn into transparent, reproducible pipelines, offering users control over analytical parameters. Additionally, OmnibusX features proprietary modules, including a highly accurate cell-type prediction engine and an interactive plotting editor for generating publication-quality visualizations. Available as a standalone desktop application and an enterprise edition for centralized server deployment, OmnibusX ensures all data processing is conducted locally, eliminating external data transfer and usage tracking. By lowering technical barriers and enhancing reproducibility, OmnibusX aims to accelerate biological discovery and foster robust, data-driven collaborations. A fully documented trial version is accessible at: https://omnibusx.com/apps.

Computational Biology↗

PubMed2026

Motif-Cluster: Motif driven prioritization of transcription factor binding clusters.

Genome-wide analyses of transcription factor (TF) motif binding sites have largely emphasized individual high-affinity sites, while overlooking the regulatory importance of locally repetitive motif clusters. Such clusters, including combinations of weak and strong binding sites, can collectively enhance TF occupancy and regulatory activity. Here we present Motif-Cluster, an open-source framework for motif-driven prioritization and visualization of TF binding clusters using sequence information alone. Motif-Cluster integrates a density-based clustering strategy with flexible modeling of binding-site gaps and affinity signals, enabling the identification and ranking of candidate regulatory regions without requiring experimental binding data. Through simulations and multiple real-data analyses, we show that combining gap distributions with binding affinity effectively balances cluster size and signal strength while reducing noise from weak sites. Application to ZNF410 successfully recovers the previously characterized binding clusters in the CHD4 promoter, which are conserved between human and mouse. Additional case studies involving PHB1, TWIST1, and EGR1 further demonstrate the general applicability of the method across diverse transcription factors. Motif-Cluster also provides intuitive visualization and reproducible workflows to facilitate interpretation of spatially dense motif patterns. Overall, Motif-Cluster offers a robust and flexible approach for prioritizing transcription factor regulatory regions from genome-wide motif scans, enabling biological discovery and guiding experimental design, particularly in settings where direct genome-wide binding assays are unavailable.

Transcription Factors↗

PubMed2026

Ten quick tips for spatial transcriptomics analysis.

Spatial transcriptomics (ST) enables genome-wide gene expression profiling while retaining spatial context within tissue sections. Since the foundational work by Ståhl et al. in 2016, the field has expanded rapidly, with diverse platforms now spanning sequencing-based (e.g., Visium, Visium HD, Slide-seq, Stereo-seq, and Seq-Scope) and imaging-based (e.g., MERFISH, Xenium, and CosMx SMI) approaches. The breadth of platforms, data structures, and computational tools, however, can be daunting for newcomers. Here, we present ten quick tips spanning the entire ST research workflow: whether ST suits a given biological question, how to select a platform aligned with study objectives, how to understand and process ST data, and which software tools to employ for analysis and visualization. We further discuss interpreting spatial patterns in biological context, integrating complementary modalities such as single-cell RNA sequencing and spatial proteomics, and leveraging public datasets and sharing results. Finally, we highlight current limitations of ST, particularly the challenge of reconstructing three-dimensional tissue architecture from serial tissue sections. This review provides biologists, bioinformaticians, and clinician-scientists with a concise, platform-neutral roadmap for incorporating ST into research, from experimental design to biological discovery.

Spatial Transcriptomics↗

PubMed2026

Methods for cost-efficient, whole genome sequencing surveillance for enhanced detection of outbreaks in a hospital setting.

INTRODUCTION: Outbreaks of healthcare-associated infections (HAI) result in substantial patient morbidity and mortality; mitigation efforts by infection prevention teams have the potential to curb outbreaks and prevent transmission to additional patients. The incorporation of whole genome sequencing (WGS) surveillance of suspected high-risk pathogens often identifies outbreaks that are not detected by traditional infection prevention methods and provides evidence for transmission. Our approach to real-time WGS surveillance, the Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT), has 1) identified serious outbreaks that were otherwise undetected and 2) shown the potential to be cost saving. METHODS: We describe our cost-efficient methods to perform WGS surveillance and data analysis of pathogens for institutions that are interested in expanding infection prevention surveillance. We provide an overview of the weekly workflow of EDS-HAT during two distinct phases over three years. RESULTS: In an average week at our tertiary healthcare system, we sequenced 60 samples at a cost of less than $100 each during Phase 1, and 80 samples for less than $70 each in Phase 2, inclusive of laboratory reagents and staff salaries. The average turnaround time, from sample collection to reporting data to infection prevention, was nine days. CONCLUSIONS: Performing EDS-HAT in real-time can be both feasible and time-efficient. Providing such timely information to aid in outbreak detection could identify transmission events sooner and thus could increase patient safety.

Disease Outbreaks↗

PubMed2005

Time-motion analysis of 6 cystic fibrosis mutation detection systems.

BACKGROUND: A dramatic increase in requests for routine cystic fibrosis (CF) carrier screening prompted us to conduct a time-motion analysis comparing commercially available CF testing platforms. Questions addressed in the study included: (a) How much time is required to perform each step involved in carrying out the assay procedure? (b) Which system requires the minimum number of manual manipulations to complete a typical run? (c) What workflow benefits can be achieved by automation? METHODS: We used a 96-sample run for comparisons and analyzed each of the 6 methods to determine the number of pipetting steps and manual manipulations, the labor and instrument time, and the total time required to perform the assay. The survey participants included a staff of 4 technologists who perform complex molecular assays regularly. Time required for each procedure was determined by direct observation and from work logs completed by the technologists. RESULTS: The total number of pipetting motions varied from 78 to 344. Labor time ranged from 2.6 to 8.4 h, and total assay time from 7.6 to 13.7 h. CONCLUSION: Time-motion analysis allowed identification of a method that minimized pipetting motions and thus reduced the risk of repetitive stress injury.

Clinical Laboratory Techniques↗

PubMed2000

Computer decision support systems.

Computer decision support systems are computer applications designed to aid clinicians in making diagnostic and therapeutic decisions in patient care. They can simplify access to data needed to make decisions, provide reminders and prompts at the time of a patient encounter, assist in establishing a diagnosis and in entering appropriate orders, and alert clinicians when new patterns in patient data are recognized. Decision support systems that present patient-specific recommendations in a form that can save clinicians time have been shown to be highly effective, sustainable tools for changing clinician behavior. Designing and implementing such systems is challenging because of the computing infrastructure required, the need for patient data in a machine-processible form, and the changes to existing workflow that may result. Despite these difficulties, there is substantial evidence from trials in a wide range of clinical settings that computer decision support systems help clinicians do a better job caring for patients. As computer-based records and order-entry systems become more common, automated decision support systems will be used more broadly.

Computer Systems↗

PubMed2006

Proteomics and laser microdissection.

Two-dimensional gel electrophoresis (2-DE) combined with protein identification by mass spectrometry (MS) is currently the method of choice in the majority of proteomic projects. Novel gel-free technologies have been developed but 2-DE remains the technique of choice for quantitative expression profiling of large sets of complex protein mixtures such as whole cell/tissue lysates. Solubilized proteins are separated in the first dimension according to their charge properties (isoelectric point, pI) by isoelectric focusing (IEF) under denaturing conditions, followed by their separation in the second dimension by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), according to their relative molecular mass (Mr). 2-DE can resolve more than 5000 proteins simultaneously (approximately 2000 proteins routinely) and can detect less than 1 ng of protein per spot. Furthermore, it delivers a map of intact proteins, which reflects changes in protein expression level, isoforms or posttranslational modifications. In this chapter we describe the various steps in the 2-DE proteomics workflow, namely sample preparation, solubilization, 2-D gel electrophoresis, protein detection and visualization, and protein identification by mass spectrometry. The use of 2-DE in conjunction with laser microdissection microscopy is presented and discussed.

Animals↗

PubMed2006

Use of hybridization probes in a real-time PCR assay on the LightCycler for the detection of methicillin-resistant Staphylococcus aureus.

The rapid and accurate identification of methicillin-resistant Staphylococcus aureus (MRSA) is of great importance for the affected patient, the involved ward, and the microbiological laboratory. Resistance to methicillin is encoded by the mecA gene in S. aureus. Because routine laboratory diagnostics may be time consuming and because species differentiation encounters a variety of difficulties, molecular techniques detecting both the mecA and a S. aureus-specific gene are used for rapid and accurate detection and identification of MRSA. Various protocols, including the manual extraction of DNA have been established. In this chapter, the identification of MRSA based on simultaneous detection of the mecA gene and the S. aureus-specific Sa442 DNA fragment using automated DNA extraction and real-time polymerase chain reaction is described. This method is an attractive alternative to labor-intensive manual protocols and can easily be incorporated into the diagnostic microbiology laboratory workflow, with the ability to obtain results within 4 h.

Bacterial Proteins↗

PubMed2006

Proteomic analysis of Dictyostelium discoideum.

The social amoeba Dictyostelium discoideum is already known as a model organism for a variety of cellular and molecular studies. Now that the genome sequencing project has been completed and different tools with which to overexpress or knock out genes are available, this species has also moved into the spotlight of functional genomics studies. Consequently, this genomic sequence information can now be exploited to realize D. discoideum proteomics projects. Here, we present validated protocols adapted for analysis of the D. discoideum proteome. The workflow described in this chapter comprises two-dimensional polyacrylamide gel electrophoresis for protein separation and peptide mass fingerprint (matrix-assisted laser desorption/ionization time-of-flight mass spectrometry) for protein identification.

Animals↗

PubMed2005

Optimal molecular profiling of tissue and tissue components: defining the best processing and microdissection methods for biomedical applications.

Isolation of well-preserved pure cell populations is a prerequisite for sound studies of the molecular basis of any tissue-based biological phenomenon. This article reviews current methods for obtaining anatomically specific signals from molecules isolated from tissues, a basic requirement for productive linking of phenotype and genotype. The quality of samples isolated from tissue and used for molecular analysis is often glossed over or omitted from publications, making interpretation and replication of data difficult or impossible. Fortunately, recently developed techniques allow life scientists to better document and control the quality of samples used for a given assay, creating a foundation for improvement in this area. Tissue processing for molecular studies usually involves some or all of the following steps: tissue collection, gross dissection/identification, fixation, processing/embedding, storage/archiving, sectioning, staining, microdissection/annotation, and pure analyte labeling/identification and quantification. We provide a detailed comparison of some current tissue microdissection technologies, and provide detailed example protocols for tissue component handling upstream and downstream from microdissection. We also discuss some of the physical and chemical issues related to optimal tissue processing, and include methods specific to cytology specimens. We encourage each laboratory to use these as a starting point for optimization of their overall process of moving from collected tissue to high quality, appropriately anatomically tagged scientific results. In optimized protocols is a source of inefficiency in current life science research. Improvement in this area will significantly increase life science quality and productivity. The article is divided into introduction, materials, protocols, and notes sections. Because many protocols are covered in each of these sections, information relating to a single protocol is not contiguous. To get the greatest benefit from this article, readers are advised to read through the entire article first, identify protocols appropriate to their laboratory for each step in their workflow, and then reread entries in each section pertaining to each of these single protocols.

Biomarkers↗

PubMed2005

Development of functional requirements for electronic health communication: preliminary results from the ELIN project.

BACKGROUND: User participation is important for developing a functional requirements specification for electronic communication. General practitioners and practising specialists, however, often work in small practices without the resources to develop and present their requirements. It was necessary to find a method that could engage practising doctors in order to promote their needs related to electronic communication. MATERIALS AND METHODS: Qualitative research methods were used, starting a process to develop and study documents and collect data from meetings in project groups. Triangulation was used, in that the participants were organised into a panel of experts, a user group, a supplier group and an editorial committee. RESULTS: The panel of experts created a list of functional requirements for electronic communication in health care, consisting of 197 requirements, in addition to 67 requirements selected from an existing Norwegian standard for electronic patient records (EPRs). Elimination of paper copies sent in parallel with electronic messages, optimal workflow, a common electronic 'envelope' with directory services for units and end-users, and defined requirements for content with the possibility of decision support were the most important requirements. CONCLUSIONS: The results indicate that we have found a method of developing functional requirements which provides valid results both for practising doctors and for suppliers of EPR systems.

Attitude of Health Personnel↗

PubMed2006

Decision support for health care: the PROforma evidence base.

Cancer Research UK has developed PROforma, a formal language for modelling clinical processes, along with associated tools for creating decision support, care planning, clinical workflow management and other applications. The PROforma method has been evaluated in a variety of settings: in primary health care (prescribing, referral of suspected cancer patients, genetic risk assessment) and in specialist care of patients with breast cancer, leukaemia, HIV infection and other conditions. About nine years of experience have been gained with PROforma technologies. Seven trials of decision support applications have been published or are in preparation. Each of these has shown significant positive effects on a variety of measures of quality and/or outcomes of care. This paper reviews the evidence base for the clinical effectiveness of these PROforma applications, and previews the CREDO project -- a multi-centre trial of a complex PROforma application for supporting integrated breast cancer care across primary and secondary care settings.

Breast Neoplasms↗