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1. What’s in a Name? Terminology Preferences Among Patients Receiving Methadone Treatment

   https://doi.org/10.1007/s11606-022-07813-w  

   Gazzola, Marina Gaeta; Maclean, Emma; Beitel, Mark; Carmichael, Iain D.; Cammack, Katharine M.; Eggert, Kathryn F.; Roehrich, Teresa; Madden, Lynn M.; Jegede, Oluwole; Zheng, Xiaoying; et al (February 2023, Journal of General Internal Medicine)
2. Assessing Cellular and Transcriptional Diversity of Ileal Mucosa Among Treatment-Naïve and Treated Crohn’s Disease

   https://doi.org/10.1093/ibd/izac201  

   Maddipatla, Sushma Chowdary; Kolachala, Vasantha L.; Venkateswaran, Suresh; Dodd, Anne F.; Pelia, Ranjit Singh; Geem, Duke; Yin, Hong; Sun, Yutong; Xu, Congmin; Mo, Angela; et al (October 2022, Inflammatory Bowel Diseases)

   Abstract BackgroundCrohn’s disease is a lifelong disease characterized by chronic inflammation of the gastrointestinal tract. Defining the cellular and transcriptional composition of the mucosa at different stages of disease progression is needed for personalized therapy in Crohn’s. MethodsIleal biopsies were obtained from (1) control subjects (n = 6), (2) treatment-naïve patients (n = 7), and (3) established (n = 14) Crohn’s patients along with remission (n = 3) and refractory (n = 11) treatment groups. The biopsies processed using 10x Genomics single cell 5' yielded 139 906 cells. Gene expression count matrices of all samples were analyzed by reciprocal principal component integration, followed by clustering analysis. Manual annotations of the clusters were performed using canonical gene markers. Cell type proportions, differential expression analysis, and gene ontology enrichment were carried out for each cell type. ResultsWe identified 3 cellular compartments with 9 epithelial, 1 stromal, and 5 immune cell subtypes. We observed differences in the cellular composition between control, treatment-naïve, and established groups, with the significant changes in the epithelial subtypes of the treatment-naïve patients, including microfold, tuft, goblet, enterocyte,s and BEST4+ cells. Surprisingly, fewer changes in the composition of the immune compartment were observed; however, gene expression in the epithelial and immune compartment was different between Crohn’s phenotypes, indicating changes in cellular activity. ConclusionsOur study identified cellular and transcriptional signatures associated with treatment-naïve Crohn’s disease that collectively point to dysfunction of the intestinal barrier with an increase in inflammatory cellular activity. Our analysis also highlights the heterogeneity among patients within the same disease phenotype, shining a new light on personalized treatment responses and strategies. 

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3. Opioid Use Disorder and COVID-19: Treatment and Recovery Factors among Vulnerable Populations at the Intersection of Two U.S. Epidemics

   https://doi.org/10.1080/02791072.2022.2079443  

   Banks, Devin E.; Paschke, Maria E.; Li, Xiao; Fentem, Andrea; Rich, Amanda; Szlyk, Hannah S.; Cavazos-Rehg, Patricia (May 2022, Journal of Psychoactive Drugs)
4. Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis

   https://doi.org/10.1038/s42256-023-00638-0  

   Liu, Ruoqi; Hunold, Katherine M.; Caterino, Jeffrey M.; Zhang, Ping (April 2023, Nature Machine Intelligence)
5. Implementing and Analyzing the Advantages of Voice AI as Measurement-Based Care (MBC) to Address Behavioral Health Treatment Disparities among Youth in Economically Disadvantaged Communities

   https://doi.org/10.30953/thmt.v9.506  

   Alemu, Yared; Cárdenas_Bautista, Elizabeth; Vinson, Sarah; Ohiomoba, Patrick; Melecio, Nakia; Amare, Hermon; Vivian, Jaya; Karippara, Harshitha (August 2024, Telehealth and Medicine Today)

   Berkowitz_L; Russo_J; Barach_P; Pedrotti_C (Ed.)

   Only 20% of behavioral health providers use measurement-based care [MBC].1 Two reasons for MBC’s low uptake outcomes are a need for stronger consensus regarding optimal use (in both frequency and consistency) and the absence of a widely utilized data analytics infrastructure. TQIntelligence has built and implemented a measurement-based system for community behavioral health providers, which includes the use of a novel AI-enabled voice algorithm designed to provide psychiatric decision and triaging support to pediatric populations. The success of the implementation and related outcomes varied depending on the organization and the therapist's involvement in the pilot.2 This paper will contribute to the literature on measurement care and its effectiveness. It also challenges the dominant narrative that such systems are too complicated and ineffective in community behavioral health that serve children and adolescents from low-income communities. 

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