Letter to the Editor: On clinical predictors of treatment-resistant depression: Comments on validation strategy, predictor independence, and clinical implementation.

Depression represents a major international public health problem for both developed and developing countries. It is associated with increased risk of morbidity, suicide, decreased physical, cognitive and social function, and greater self-neglect, which in turn is associated with increased mortality. The study objective was to determine the clinical characteristics and predictors of depression among the elderly attending the family medicine clinic of Usmanu Danfodiyo University Teaching Hospital, Sokoto, Northwestern Nigeria. The study was a hospital-based cross-sectional study of 391 elderly participants in the family medicine clinic of Usmanu Danfodiyo University Teaching Hospital, recruited through systematic sampling technique. Data was collected using a pre-tested, structured interviewer-administered questionnaire, and Folstein (MMSE). The GDS-30 was used to screen for depression. BMI was calculated and data analyzed using SPSS version 20. Test of associations was done using the Chi-square test, and logistic regression was done; P < 0.05 was considered statistically significant. The prevalence of depression among the elderly was found to be 41.2%. Following logistic regression, being married (OR=0.559, 95% CI=0.321-0.974), bereavement in the last six months (OR=2.35, 95% CI=1.138- 4.480), poor subjective health status (OR=0.141, 95% CI=0.057-0.348), and abnormal BMI (OR=0.425, 95% CI=0.221-0.818) were found to be predictors of elderly depression in the study. The prevalence of depression among older adults was high, and the study recommends early screening, detection and management of risk factors of depressive disorder in older adults by primary care physicians.

Although substantial progress has been made in establishing evidence-based psychosocial clinical interventions and implementation strategies for mental health, translating research into practice-particularly in more accessible, community settings-has been slow. This protocol outlines the renewal of the National Institute of Mental Health-funded University of Washington Advanced Laboratories for Accelerating the Reach and Impact of Treatments for Youth and Adults with Mental Illness Center, which draws from human-centered design (HCD) and implementation science to improve clinical interventions and implementation strategies. The Center's second round of funding (2023-2028) focuses on using the Discover, Design and Build, and Test (DDBT) framework to address 3 priority clinical intervention and implementation strategy mechanisms (ie, usability, engagement, and appropriateness), which we identified as challenges to implementation and scalability during the first iteration of the center. Local redesign teams work collaboratively and share decision-making to carry out DDBT. All 4 core studies received institutional review board approval by June 2024, and each pilot project will pursue institutional review board approval when awarded. We will provide research infrastructure to 1 large effectiveness study and 3 exploratory pilot studies as part of the center grant. At least 4 additional small pilot studies will be solicited and funded by the center. All studies will explore the use of DDBT for clinical interventions and implementation strategies to identify modification targets to improve usability, engagement, and appropriateness in accessible nonspecialty settings (Discover phase); develop redesign solutions with local teams to address modification targets (Design and Build phase); and determine if redesign improves usability, engagement, and appropriateness (Test phase), as well as implementation outcomes. Center staff will collaborate with local redesign teams to develop and test clinical interventions and implementation strategies for community settings. We will collaborate with teams to use methods and centerwide measures that facilitate cross-project analysis of the effects of DDBT-driven redesign on outcomes of interest. As of January 2025, three of the 4 core studies are underway. We will generate additional evidence on the robustness of DDBT and whether combining HCD and implementation science is an asset for improving clinical interventions and implementation strategies. During the first round of the center, we established that DDBT is a useful approach to systematically identify and address chronic challenges of implementing clinical interventions and implementation strategies. In this subsequent grant, we expect to increase evidence of DDBT's impact on clinical interventions and implementation strategies by expanding a list of common challenges that could benefit from modification, a list of exemplary solutions to address these challenges, and guidance on using the DDBT framework. These resources will contribute to broader discourse on how to enhance implementation of clinical interventions and implementation strategies that integrate HCD and implementation science. PRR1-10.2196/65446.

Comparative Analysis of Artificial Intelligence Methods in Clinical Implementation: A Review of Techniques, Validation Strategies, and Success Metrics

Point-of-care technology (POCT) provides actionable information at the site of care to allow rapid clinical decision-making. With healthcare emphasis shifting toward precision medicine, population health, and chronic disease management, the potential impact of POCT continues to grow, and several prominent POCT trends have emerged or strengthened in the last decade. This review summarizes current and emerging trends in POCT, including technologies approved or cleared by the Food and Drug Administration or in development. Technologies included have either impacted existing clinical diagnostics applications (e.g., continuous monitoring and targeted nucleic acid testing) or are likely to impact diagnostics delivery in the near future. The focus is limited to in vitro diagnostics applications, although in some sections, technologies beyond in vitro diagnostics are also included given the commonalities (e.g., ultrasound plug-ins for smart phones). For technologies in development (e.g., wearables, noninvasive testing, mass spectrometry and nuclear magnetic resonance, paper-based diagnostics, nanopore-based devices, and digital microfluidics), we also discuss their potential clinical applications and provide perspectives on strategies beyond technological and analytical proof of concept, with the end goal of clinical implementation and impact. The field of POCT has witnessed strong growth over the past decade, as evidenced by new clinical or consumer products or research and development directions. Combined with the appropriate strategies for clinical needs assessment, validation, and implementation, these and future POCTs may significantly impact care delivery and associated outcomes and costs.

PurposeThis study focused on identifying a hereditary predisposition in women previously diagnosed with early-onset breast cancer through a retrospective outreach activity (Traceback). The objectives were to evaluate the possible clinical implementation of a simplified Traceback strategy and to identify carriers of pathogenic variants among previously untested women.MethodsThree hundred and fifteen Traceback-eligible women diagnosed with breast cancer at 36–40 years in Southern Sweden between 2000 and 2019 were identified and offered an analysis of the genes ATM, BARD1, BRCA1, BRCA2, CHEK2, PALB2, RAD51C, and RAD51D through a standardized letter. Women who chose to participate were asked about their experiences through a questionnaire. The workload for the study personnel was measured and recorded.ResultsOne hundred and seventy-six women underwent genetic testing and pathogenic variants were identified in 9.7%: ATM (n = 6), BARD1 (n = 1), BRCA1 (n = 3), CHEK2 (n = 5), and PALB2 (n = 2). Women with normal test results were informed through a standardized letter. Carriers of pathogenic variants were contacted by telephone and offered in-person genetic counseling. One hundred and thirty-four women returned the subsequent questionnaire. Most study participants were satisfied with both written pre- and post-test information and many expressed their gratitude. The extra workload as compared to routine clinical genetic counseling was modest (8 min per patient).ConclusionThe insights from the participants’ perspectives and sentiments throughout the process support the notion that the Traceback procedure is a safe and an appreciated complement to routine genetic counseling. The genetic yield of almost 10% also suggests that the associated extra workload for genetic counselors could be viewed as acceptable in clinical implementation scenarios.

There is increasing evidence that major depression impacts the course of HIV infection, yet few studies have explored demographic and clinical predictors of depression in people who with HIV/AIDS. This study investigated predictors of depression (e.g., demographic and clinical variables, negative life events, and coping response) among outpatients with recently diagnosed HIV/AIDS patients in South Africa. One hundred forty-nine recently diagnosed HIV/AIDS patients (44 males and 105 females; mean time since diagnosis = 5.8, standard deviation [SD] 4.1) were evaluated. Subjects were assessed using the Mini International Neuropsychiatric Interview (MINI), the Carver Brief COPE coping scale, and the Sheehan Disability Scale. In addition, previous exposures to trauma and past risk behaviors were assessed. Three variables: gender (odd ratio [OR] = 1.23; 95% confidence interval [CI] 1.56, 1.93), impact of negative life events (OR = 1.13; CI, 1.03, 1.23), and disability (OR = 1.51, CI, 1.28, 1.80) predicted current major depression. It is well known from non-HIV populations that female gender and increased negative life events predict depression. These data also emphasize the importance of these links in HIV.

Background:Hypothermia therapy has been suggested to attenuate myocardial necrosis; however, the clinical implementation as a valid therapeutic strategy has failed, and new approaches are needed to translate into clinical applications. This study aimed to assess the feasibility, safety, and efficacy of a novel selective intracoronary hypothermia (SICH) device in mitigating myocardial reperfusion injury.Methods:This study comprised two phases. The first phase of the SICH was performed in a normal porcine model for 30 minutes (n = 5) to evaluate its feasibility. The second phase was conducted in a porcine myocardial infarction (MI) model of myocardial ischemia/reperfusion which was performed by balloon occlusion of the left anterior descending coronary artery for 60 minutes and maintained for 42 days. Pigs in the hypothermia group (n = 8) received hypothermia intervention onset reperfusion for 30 minutes and controls (n = 8) received no intervention. All animals were followed for 42 days. Cardiac magnetic resonance analysis (five and 42 days post-MI) and a series of biomarkers/histological studies were performed.Results:The average time to lower temperatures to a steady state was 4.8 ± 0.8 s. SICH had no impact on blood pressure or heart rate and was safely performed without complications by using a 3.9 F catheter. Interleukin-6 (IL-6), tumor necrosis factor-α, C-reactive protein (CRP), and brain natriuretic peptide (BNP) were lower at 60 min post perfusion in pigs that underwent SICH as compared with the control group. On day 5 post MI/R, edema, intramyocardial hemorrhage, and microvascular obstruction were reduced in the hypothermia group. On day 42 post MI/R, the infarct size, IL-6, CRP, BNP, and matrix metalloproteinase-9 were reduced, and the ejection fraction was improved in pigs that underwent SICH.Conclusions:The SICH device safely and effectively reduced the infarct size and improved heart function in a pig model of MI/R. These beneficial effects indicate the clinical potential of SICH for treatment of myocardial reperfusion injury.

Improved cancer prognosis begins with the collection of high-quality data from clinical trials. However, the execution of longitudinal immune phenotyping studies by flow cytometry is complex, primarily due to logistical challenges that could affect data quality. The challenges of immune monitoring are exacerbated when considering remote and rural communities in Australia, which are burdened by both an increased prevalence and worse prognosis of cancer. Paradoxically, these communities are often underrepresented in clinical trials. Our group has developed a novel blood-based “immune signature” that robustly predicts failure to make a clinical response to checkpoint therapies targeting the PD-1/PD-L1 pathway in melanoma and lung cancer. These groundbreaking findings were achieved through applying a comprehensive 38-parameter immunophenotyping CyTOF™ panel to biobanked peripheral immune cells from cancer patients. We now aim to increase our clinical implementation in remote settings, with an expanded CyTOF panel of 50-plus-parameter analysis. Recent developments in CyTOF technology facilitate a highly simplified workflow of asynchronous sample collection and staining, compatible with current hospital laboratories that typically run on unpredictable schedules, to remote settings. This is uniquely enabled by using a dried-down cocktail of CyTOF antibodies that are stable, simple to use and yield reproducible staining while minimizing sample required. Furthermore, this workflow, unique to mass cytometry, provides flexibility and minimizes technical variation via sample barcoding and freezing of stained samples for shipment to a central site for batch acquisition. As such, this study is designed to ultimately demonstrate both clinical impact of the large panel and utility of CyTOF technology for a highly simplified and robust workflow for multi-site clinical trials. Here we present findings from the implementation of this novel workflow through a 100-sample pilot study. Remote asynchronous sample collection and surface staining was performed at five sites across Australia. Stained samples were then shipped to a central lab for multiplex barcoding and intracellular staining of several key functional markers. A combination of manual gating and automated high-dimensional clustering was performed in CellEngine, with principal component analysis of all samples demonstrating harmony across the sites. Overall, this workflow enables access to underserved communities, facilitating for the first time equity and scalability of immune phenotyping studies to harness truly geographically dispersed clinical centers. Citation Format: Natalie Smith, Michael Cohen, Julie Alipaz, Christina Loh, David King, Steven Kao, Sandra Taylor, Rajat Rai, Barbara Fazekas de St Groth, Helen McGuire. Implementation of a novel immune monitoring strategy for multicenter clinical trials to enable equity in cancer prognosis for individuals from remote settings of Australia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2349.

In clinical practice, ketamine, an antagonist of the N-methyl-D-aspartate receptor (NMDAR), is used to alleviate depressive symptoms in patients with major depressive disorder (MDD), especially in those with treatment-resistant depression (TRD). Accordingly, the human gene coding for the 2B subunit of the NMDAR (GRIN2B) is considered a promising candidate gene for MDD susceptibility. The primary aim of this study is to examine whether potentially functional polymorphisms of GRIN2B confer risk for MDD, and second to investigate whether GRIN2B acts as a genetic predictor for TRD in MDD patients. We enrolled 178 TRD and 612 non-TRD patients as well as 779 healthy controls. Four potentially functional polymorphisms (rs1805502, rs890, rs1806201, and rs7301328) within GRIN2B were genotyped in all participants. The haplotype analysis found significant differences in the distribution of the G-T haplotype between the TRD and control groups (corrected P = 0.007), and the frequency of the G-T haplotype in TRD group was significantly higher than that in the controls (TRD/control ratio 0.31:0.21). Statistically significant differences in allele and genotype frequencies were detected between TRD and non-TRD groups for the rs1805502 polymorphism within GRIN2B. There was a significant allelic association between rs1805502 and TRD with an excess of the G allele in the TRD group, compared to non-TRD group (OR = 1.55, 95% CI = 1.18-2.05, corrected P = 0.008). These initial findings strengthen the hypothesis that GRIN2B not only confers susceptibility to TRD, but also plays a genetic predictor for TRD in MDD patients.

BackgroundDepression is a debilitating mental health condition which affects an estimated 350 million people worldwide annually. Nurses are twice as likely to suffer from depression than professionals in other professions. This leads to a considerable loss of efficiency and productivity. We sought to determine the prevalence and predictors of depression among nurses in Cameroon.MethodsCross-sectional analysis carried out over 6 months (January – June 2018) using nurses from public and private healthcare institutions sampled consecutively in the two English-speaking regions (North west and South west regions) of Cameroon. The nurses were handed a structured, printed, self-administered questionnaire to fill and hand in at their earliest convenience. Depression and burnout were assessed using the Patient Health Questionnaire – 9 and the Oldenburg Burnout Inventory respectively.ResultsA total of 143 nurses were recruited (mean age: 29.75 ± 6.55 years; age range: 20–55 years, 32.87% male). The overall prevalence of depression was 62.24%. Independent predictors of depression after multivariable analysis were: Number of night shifts a week (adjusted odds ratio: 1.58; p value: 0.045, 95% CI; 1.01, 2.48) and Total Oldenburg Burnout Inventory score (adjusted odds ratio: 1.21, p value: 0.001; 95% CI; 1.08, 1.35). Recreational drug use was also found to perfectly predict the outcome – depression.ConclusionDepression is highly prevalent among nurses in the English-speaking regions of Cameroon. Accurate predictors could prove vital for early detection and management of affected individuals. Predictors presented herein require further investigation via multicentric nationwide studies, to obtain more generalizable results.

The authors describe the development of the Clinical Strategies Implementation Scale (CSI), an instrument designed to help providers measure the extent to which evidence-based strategies have been implemented in the treatment of persons with schizophrenia spectrum disorders. Nine ordinal scales were devised to measure key aspects of treatment strategies that have been associated with clinical and social recovery from schizophrenia: goal- and problem-oriented assessment, medication strategies, assertive case management, mental health education, caregiver-based problem solving, living skills training, psychological strategies for residual problems, crisis prevention and intervention, and booster sessions. A study of interrater reliability was conducted with 15 trained raters from participating centers in Athens, Auckland, Bonn, Budapest, Gothenburg, and Tokyo who assessed 54 cases. Each treatment strategy was weighted according to its effect size in clinical trials. Correlation analyses were conducted to explore associations between the total CSI score and ratings of clinical, social, and caregiver outcomes each year over four years of continued treatment of 51 patients. Interrater reliability ranged from .93 to .99. Four annual total CSI ratings were significantly correlated with impairment, disability, functioning, work activity, and an index of recovery. Most correlations were stronger in years 3 and 4 than in years 1 and 2. Reliable and valid assessment of the implementation of evidence-based strategies in clinical practice is feasible. The quality of integrated program implementation may be associated with improved clinical and social recovery from schizophrenic disorders.

Treatment-Resistant Depression (TRD) is a complex clinical condition characterized by inadequate response to conventional antidepressant treatments. There is growing evidence that microRNAs (miRNAs) play a role in the underlying pathophysiology of TRD and may offer new avenues for diagnostics and therapy. A structured literature review of peer-reviewed publications indexed in PubMed, Scopus, and Web of Science was conducted. The search strategy included combinations of keywords such as "treatment- resistant depression," "microRNAs," "biomarkers," and "miRNA-based interventions." Articles were selected based on relevance to miRNA expression patterns in TRD, therapeutic modulation, and their clinical potential. Dysregulation of several miRNAs-including miR-135a, miR-34a, and miR-155-was consistently observed in patients with TRD. These miRNAs were linked to impaired synaptic plasticity and persistent neuroinflammation. Therapeutic approaches using miRNA mimics or inhibitors showed potential in restoring neurobiological balance and enhancing response to traditional antidepressants. However, delivery system limitations and blood-brain barrier penetration remain significant challenges. miRNAs appear to play a dual role in TRD, serving both as biomarkers for diagnosis and as targets for novel therapies. Integrating miRNA profiling into clinical workflows could enhance diagnostic precision and guide individualized treatment strategies. Translational barriers, such as delivery specificity and standardization of detection protocols, must be addressed before the widespread clinical application of this technology. This review highlights miRNAs as promising diagnostic and therapeutic tools in TRD. Continued advancements in delivery systems and validation of biomarker panels may pave the way for their clinical implementation in personalized psychiatry.

Treatment-resistant depression (TRD) occurs in ~30% of patients with major depressive disorder (MDD) but the genetics of TRD was previously poorly investigated. Whole exome sequencing and genome-wide genotyping were available in 1209 MDD patients after quality control. Antidepressant response was compared to non-response to one treatment and non-response to two or more treatments (TRD). Differences in the risk of carrying damaging variants were tested. A score expressing the burden of variants in genes and pathways was calculated weighting each variant for its functional (Eigen) score and frequency. Gene-based and pathway-based scores were used to develop predictive models of TRD and non-response using gradient boosting in 70% of the sample (training) which were tested in the remaining 30% (testing), evaluating also the addition of clinical predictors. Independent replication was tested in STAR*D and GENDEP using exome array-based data. TRD and non-responders did not show higher risk to carry damaging variants compared to responders. Genes/pathways associated with TRD included those modulating cell survival and proliferation, neurodegeneration, and immune response. Genetic models showed significant prediction of TRD vs. response and they were improved by the addition of clinical predictors, but they were not significantly better than clinical predictors alone. Replication results were driven by clinical factors, except for a model developed in subjects treated with serotonergic antidepressants, which showed a clear improvement in prediction at the extremes of the genetic score distribution in STAR*D. These results suggested relevant biological mechanisms implicated in TRD and a new methodological approach to the prediction of TRD.

Neonicotinoids (NEOs) are used for the phytosanitary treatment of Mentha Spicata.L crops, and this practice requires precise control of these harmful substances at very low concentrations. The objective of this study is to apply an approach allowing simultaneously validation and evaluation of measurement uncertainty based on total error methodology, in order to accurately quantify the presence of two NEOs in Mentha Spicata.L utilizing a Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS)-LC-MS/MS methodology. The quantification of imidacloprid and acetamiprid employing a QuEChERS extraction method, coupled with LC-MS/MS, ensuring the accuracy of the analytical method and managing the risks associated with its routine use.A complete and exhaustive validation approach based on the "β-content, γ-confidence" tolerance interval was used for the uncertainty assessment, using the generalized pivot quantity (GPQ) concept and Monte Carlo simulation, which avoids the need for additional data while achieving intermediate precision for each concentration level within predetermined acceptable limits. The validation procedure is based on the choice of a quadratic model for the two NEOs, allowing the validation of acetamiprid and imidacloprid by LC-MS/MS assay within the range of working concentration. The flexibility of the uncertainty profile intervals was demonstrated with a variation in β-content values (66.7, 80, and 90%) and risk values (10 and 5%), which remained within the acceptability limits of 20%, and the relative expanded uncertainty did not exceed 15 and 11%. A QuEChERS-LC-MS/MS method for the analysis of two NEOs has been successfully fully validated using the uncertainty profile strategy. Implementation of an overall validation strategy, which involves both the validation and uncertainty assessment known as the uncertainty profile, for the quantification of two important NEOs in Mentha Spicata.L using QuEChERS-LC-MS/MS. This qualimetric approach has been conducted by computing the measurement uncertainty of the method utilizing data from analytical validation under conditions of intermediate precision at each level of concentration without additional effort. After that we have demonstrated the flexibility of this strategy for the LC-MS/MS quantification of acetamiprid and imidacloprid, using a decision tool that enables the choice and modification of β-content and γ-confidence values.

There is a critical unmet need in the clinical implementation of valid preventative and therapeutic strategies for patients with articular cartilage pathology based on the significant gap in understanding of the relationships between diagnostic data, disease progression, patient-related variables, and symptoms. In this article, the current state of classification and categorization for articular cartilage pathology is discussed with particular focus on machine learning methods and the authors propose a bedside-bench-bedside approach with highly quantitative techniques as a solution to these hurdles. Leveraging computational learning with available data toward articular cartilage pathology patient phenotyping holds promise for clinical research and will likely be an important tool to identify translational solutions into evidence-based clinical applications to benefit patients. Recommendations for successful implementation of these approaches include using standardized definitions of articular cartilage, to include characterization of depth, size, location, and number; using measurements that minimize subjectivity or validated patient-reported outcome measures; considering not just the articular cartilage pathology but the whole joint, and the patient perception and perspective. Application of this approach through a multistep process by a multidisciplinary team of clinicians and scientists holds promise for validating disease mechanism-based phenotypes toward clinically relevant understanding of articular cartilage pathology for evidence-based application to orthopaedic practice.