Patient and Physician Perspectives on Discussing Addiction Recovery and Spirituality in the Primary Care Setting.

BACKGROUND: Spirituality is an important component of recovery for many individuals with substance use disorder (SUD). However, few studies have compared patient and physician attitudes on spirituality in SUD recovery. OBJECTIVE: This study investigates patient and physician beliefs about the role of spirituality in SUD recovery and about discussing spirituality in relationship to recovery in primary care settings. DESIGN: Semi-structured interviews were conducted with primary care physicians recruited at two academic hospitals, and patients recruited from a faith-based residential therapeutic community. Interview transcripts were independently coded by two researchers and a grounded theory approach was used to generate themes that reflected participants' experiences. KEY RESULTS: Interviews were conducted with 15 patients and 10 physicians. Patients had diverse views about the impact of spirituality on their SUD recovery, including positive, negative, and neutral. Patient and physician opinions on discussing spirituality in a primary care setting differed: most physicians felt positively towards this, believing that understanding their patients' spirituality helped them care for their patients as whole people. Many patients felt neutral, stating that they did not feel like these conversations were necessary for their care, and that they believed physicians preferred not to discuss spirituality in medical settings. Tolerance from both the patient and physician, open-ended questioning, and an individualized approach were identified as facilitators to effective discussions about spirituality and recovery. CONCLUSIONS: Spirituality can have diverse effects on an individual's SUD recovery. Physicians endorsed the benefits of discussing spirituality in the context of their patients' recovery, while patients expressed reservations about engaging in these conversations with their physicians. This variation in perspectives highlights the need for additional research to understand the individual and structural factors that contribute to it, as well as best practices for engaging in effective, non-judgmental conversations about spirituality in recovery.

DNA Sequence-Based Approach for Classifying the Mold Status of Buildings.

Dampness or water damage in buildings and human exposure to the resultant mold growth is an ever-present public health concern. This study provides quantitative evidence that the airborne fungal ecology of homes with known mold growth ("moldy") differs from the normal airborne fungal ecology of homes with no history of dampness, water damage, or visible mold ("no mold"). Settled dust from indoor air and outdoor air and direct samples from building materials with mold growth were examined in homes from 11 cities across dry, temperate, and continental climate regions within the United States. Community analysis based on the sequence of the internal transcribed spacer region of fungal ribosomal RNA encoding genes demonstrated consistent and quantifiable differences between the fungal ecology of settled dust in homes with inspector-verified water damage and visible mold versus the settled dust of homes with no history of dampness, water damage, or visible mold. These differences include lower community richness (padj = 0.01) in the settled dust of moldy homes versus no mold homes, as well as distinct community taxonomic structures between moldy and no mold homes (ANOSIM, R = 0.15, p = 0.001). We identified 11 Ascomycota taxa that were more highly enriched in moldy homes and 14 taxa from Ascomycota, Basidiomycota, and Zygomycota that were more highly enriched in no mold homes. The indoor air differences between moldy versus no mold homes were significant for all three climate regions considered. These distinct but complex differences between settled dust samples from moldy and no homes were used to train a machine learning-based model to classify the mold status of a home. The model was able to accurately classify 100% of moldy homes and 90% of no mold homes. The integration of DNA-based fungal ecology with advanced computational approaches can be used to accurately classify the presence of mold growth in homes, assist with inspection and remediation decisions, and potentially lead to reduced exposure to hazardous microbes indoors.

Validation and characterization of a novel method for selective vagal deafferentation of the gut.

There is a lack of tools that selectively target vagal afferent neurons (VAN) innervating the gut. We use saporin (SAP), a potent neurotoxin, conjugated to the gastronintestinal (GI) hormone cholecystokinin (CCK-SAP) injected into the nodose ganglia (NG) of male Wistar rats to specifically ablate GI-VAN. We report that CCK-SAP ablates a subpopulation of VAN in culture. In vivo, CCK-SAP injection into the NG reduces VAN innervating the mucosal and muscular layers of the stomach and small intestine but not the colon, while leaving vagal efferent neurons intact. CCK-SAP abolishes feeding-induced c-Fos in the NTS, as well as satiation by CCK or glucagon like peptide-1 (GLP-1). CCK-SAP in the NG of mice also abolishes CCK-induced satiation. Therefore, we provide multiple lines of evidence that injection of CCK-SAP in NG is a novel selective vagal deafferentation technique of the upper GI tract that works in multiple vertebrate models. This method provides improved tissue specificity and superior separation of afferent and efferent signaling compared with vagotomy, capsaicin, and subdiaphragmatic deafferentation.NEW & NOTEWORTHY We develop a new method that allows targeted lesioning of vagal afferent neurons that innervate the upper GI tract while sparing vagal efferent neurons. This reliable approach provides superior tissue specificity and selectivity for vagal afferent over efferent targeting than traditional approaches. It can be used to address questions about the role of gut to brain signaling in physiological and pathophysiological conditions.

Utilizing a composite citation index for evaluating clinical ophthalmology research: insights into gender, nationality, and self-citation among top ophthalmology researchers.

OBJECTIVES: To compare the performance of a composite citation score (c-score) and its six constituent citation indices, including H-index, in predicting winners of the Weisenfeld Award in ophthalmologic research. Secondary objectives were to explore career and demographic characteristics of the most highly cited researchers in ophthalmology. METHODS: A publicly available database was accessed to compile a set of top researchers in the field of clinical ophthalmology and optometry based on Scopus data from 1996 to 2021. Each citation index was used to construct a multivariable model adjusted for author demographic characteristics. Using area under the receiver operating curve (AUC) analysis, each index's model was evaluated for its ability to predict winners of the Weisenfeld Award in Ophthalmology, a research distinction presented by the Association for Research in Vision and Ophthalmology (ARVO). Secondary analyses investigated authors' self-citation rates, career length, gender, and country affiliation over time. RESULTS: Approximately one thousand unique authors publishing primarily in clinical ophthalmology/optometry were analyzed. The c-score outperformed all other citation indices at predicting Weisenfeld Awardees, with an AUC of 0.99 (95% CI: 0.97-1.0). The H-index had an AUC of 0.89 (95% CI: 0.83-0.96). Authors with higher c-scores tended to have longer career lengths and similar self-citation rates compared to other authors. Sixteen percent of authors in the database were identified as female, and 64% were affiliated with the United States of America. CONCLUSION: The c-score is an effective metric for assessing research impact in ophthalmology, as seen through its ability to predict Weisenfeld Awardees.

Detecting Visual Field Worsening From Optic Nerve Head and Macular Optical Coherence Tomography Thickness Measurements.

Purpose: Compare the use of optic disc and macular optical coherence tomography measurements to predict glaucomatous visual field (VF) worsening. Methods: Machine learning and statistical models were trained on 924 eyes (924 patients) with circumpapillary retinal nerve fiber layer (cp-RNFL) or ganglion cell inner plexiform layer (GC-IPL) thickness measurements. The probability of 24-2 VF worsening was predicted using both trend-based and event-based progression definitions of VF worsening. Additionally, the cp-RNFL and GC-IPL predictions were combined to produce a combined prediction. A held-out test set of 617 eyes was used to calculate the area under the curve (AUC) to compare cp-RNFL, GC-IPL, and combined predictions. Results: The AUCs for cp-RNFL, GC-IPL, and combined predictions with the statistical and machine learning models were 0.72, 0.69, 0.73, and 0.78, 0.75, 0.81, respectively, when using trend-based analysis as ground truth. The differences in performance between the cp-RNFL, GC-IPL, and combined predictions were not statistically significant. AUCs were highest in glaucoma suspects using cp-RNFL predictions and highest in moderate/advanced glaucoma using GC-IPL predictions. The AUCs for the statistical and machine learning models were 0.63, 0.68, 0.69, and 0.72, 0.69, 0.73, respectively, when using event-based analysis. AUCs decreased with increasing disease severity for all predictions. Conclusions: cp-RNFL and GC-IPL similarly predicted VF worsening overall, but cp-RNFL performed best in early glaucoma stages and GC-IPL in later stages. Combining both did not enhance detection significantly. Translational Relevance: cp-RNFL best predicted trend-based 24-2 VF progression in early-stage disease, while GC-IPL best predicted progression in late-stage disease. Combining both features led to minimal improvement in predicting progression.

Circulating Biomarkers for the Early Diagnosis and Management of Hepatocellular Carcinoma with Potential Application in Resource-Limited Settings.

Hepatocellular carcinoma (HCC) is among the world's third most lethal cancers. In resource-limited settings (RLS), up to 70% of HCCs are diagnosed with limited curative treatments at an advanced symptomatic stage. Even when HCC is detected early and resection surgery is offered, the post-operative recurrence rate after resection exceeds 70% in five years, of which about 50% occur within two years of surgery. There are no specific biomarkers addressing the surveillance of HCC recurrence due to the limited sensitivity of the available methods. The primary goal in the early diagnosis and management of HCC is to cure disease and improve survival, respectively. Circulating biomarkers can be used as screening, diagnostic, prognostic, and predictive biomarkers to achieve the primary goal of HCC. In this review, we highlighted key circulating blood- or urine-based HCC biomarkers and considered their potential applications in resource-limited settings, where the unmet medical needs of HCC are disproportionately highly significant.

Predicting daily COVID-19 case rates from SARS-CoV-2 RNA concentrations across a diversity of wastewater catchments.

We assessed the relationship between municipality COVID-19 case rates and SARS-CoV-2 concentrations in the primary sludge of corresponding wastewater treatment facilities. Over 1700 daily primary sludge samples were collected from six wastewater treatment facilities with catchments serving 18 cities and towns in the State of Connecticut, USA. Samples were analyzed for SARS-CoV-2 RNA concentrations during a 10 month time period that overlapped with October 2020 and winter/spring 2021 COVID-19 outbreaks in each municipality. We fit lagged regression models to estimate reported case rates in the six municipalities from SARS-CoV-2 RNA concentrations collected daily from corresponding wastewater treatment facilities. Results demonstrate the ability of SARS-CoV-2 RNA concentrations in primary sludge to estimate COVID-19 reported case rates across treatment facilities and wastewater catchments, with coverage probabilities ranging from 0.94 to 0.96. Lags of 0 to 1 days resulted in the greatest predictive power for the model. Leave-one-out cross validation suggests that the model can be broadly applied to wastewater catchments that range in more than one order of magnitude in population served. The close relationship between case rates and SARS-CoV-2 concentrations demonstrates the utility of using primary sludge samples for monitoring COVID-19 outbreak dynamics. Estimating case rates from wastewater data can be useful in locations with limited testing availability, testing disparities, or delays in individual COVID-19 testing programs.