Large-scale Validation Research Articles

Funding Success of United States Federal Grant Applications Proposing to Study Therapeutic Applications of Psychedelics: A Survey Study

The author surveyed researchers about United States federal grant applications for therapeutic psychedelic research and their funding success. An anonymous survey was sent to corresponding authors of the 50 most-cited psychedelic research articles published after 2000 and also disseminated on Twitter. Ten researchers responded, reporting on 24 National Institutes of Health (NIH) grant applications for psilocybin, ibogaine, lysergic acid diethylamide (LSD), 3,4-methylenedioxymethamphetamine (MDMA), and other psychedelics, dating back to the early 1990s. Grant applications increased noticeably after 2006. Of the applications assessed, 16.7% were funded, below the NIH’s 23.4% average funding rate for R01-equivalent grants from 1998 to 2023. While no applications submitted before 2006–2010 were funded, the funding rate since then (19.05–22.2%) aligns with the NIH’s 20.6 ± 1.9% annual average for R01-equivalent grants from 2006 to 2023. Respondents generally perceived funding for psychedelic research as more difficult to obtain than for other areas, though recent improvements were noted. If the analyzed applications represent only a small subset of total submissions of applications proposing to study therapeutic applications of psychedelics, the findings may have limited generalizability and larger-scale validation studies would be required. However, this is difficult to determine since detailed data on unfunded NIH applications are not publicly available.

Archaea methanogens are associated with cognitive performance through the shaping of gut microbiota, butyrate and histidine metabolism.

The relationship between bacteria, cognitive function and obesity is well established, yet the role of archaeal species remains underexplored. We used shotgun metagenomics and neuropsychological tests to identify microbial species associated with cognition in a discovery cohort (IRONMET, n = 125). Interestingly, methanogen archaeas exhibited the strongest positive associations with cognition, particularly Methanobrevibacter smithii (M. smithii). Stratifying individuals by median-centered log ratios (CLR) of M. smithii (low and high M. smithii groups: LMs and HMs) revealed that HMs exhibited better cognition and distinct gut bacterial profiles (PERMANOVA p = 0.001), characterized by increased levels of Verrucomicrobia, Synergistetes and Lentisphaerae species and reduced levels of Bacteroidetes and Proteobacteria. Several of these species were linked to the cognitive test scores. These findings were replicated in a large-scale validation cohort (Aging Imageomics, n = 942). Functional analyses revealed an enrichment of energy, butyrate, and bile acid metabolism in HMs in both cohorts. Global plasma metabolomics by CIL LC-MS in IRONMET identified an enrichment of methylhistidine, phenylacetate, alpha-linolenic and linoleic acid, and secondary bile acid metabolism associated with increased levels of 3-methylhistidine, phenylacetylgluamine, adrenic acid, and isolithocholic acid in the HMs group. Phenylacetate and linoleic acid metabolism also emerged in the Aging Imageomics cohort performing untargeted HPLC-ESI-MS/MS metabolic profiling, while a targeted bile acid profiling identified again isolithocholic acid as one of the most significant bile acid increased in the HMs. 3-Methylhistidine levels were also associated with intense physical activity in a second validation cohort (IRONMET-CGM, n = 116). Finally, FMT from HMs donors improved cognitive flexibility, reduced weight, and altered SCFAs, histidine-, linoleic acid- and phenylalanine-related metabolites in the dorsal striatum of recipient mice. M. smithii seems to interact with the bacterial ecosystem affecting butyrate, histidine, phenylalanine, and linoleic acid metabolism with a positive impact on cognition, constituting a promising therapeutic target to enhance cognitive performance, especially in subjects with obesity.

Nutrition Care Model for Nutritional Assessment and Dietary Counseling of Adults Living with HIV in India

Abstract Background: A high prevalence of undernutrition (40%) exists among adults living with HIV (ALHIV) in India due to their increased metabolic demands. Diminished nutritional status in turn has been associated with increased risk of morbidity and mortality among ALHIV. Nutritional assessment and its management are an integral part of HIV care; however, no strategy exists for screening of undernutrition among ALHIV. Materials and Methods: The nutrition care model has been developed for undertaking nutritional assessment by classifying the severity of undernutrition based on body mass index (BMI) and identification of early signs of undernutrition including anemia and hypoalbuminemia among ALHIV. Results and Conclusion: The model suggests energy and protein intake based on the BMI and indicates desirable body weight corresponding to a BMI of 21 kg/m2. Based on these requirements, the portion size of each major food group is provided for individualized dietary counseling by frontline workers. ALHIV found with signs and symptoms of anemia, low hemoglobin, and serum albumin are referred to the nearest health facility. Large-scale operational research and field validation studies are planned to better understand the effectiveness of the model in improving the nutritional and clinical outcomes and the strengths and limitations as an operational tool in programmatic settings.

P0342 Quantitative faecal immunochemical test (qFIT) is comparable to calprotectin as a biomarker of IBD: Prospective longitudinal analysis of disease activity and endoscopic mucosal healing

Abstract Background Quantitative faecal immunochemical test (qFIT) to detect stool haemoglobin is used for colorectal cancer screening at a population level in UK (& Europe) with high patient acceptability due to its ease of use and relative low cost . We evaluate the clinical utility of qFIT with faecal calprotectin (fCal) in IBD. Methods UC and CD patients from the prospective, multi-centre longitudinal MUSIC study (2020-2024; www.musicstudy.uk) were included. qFIT and fCal samples were collected every three months at five time points. qFIT analysis was conducted using the HM-JACKarc automated analyser. Blinded qFIT results were compared with physician global assessment, clinical indices (HBI, SCCAI)*, and endoscopic findings. All patients had active disease at baseline, with a repeat colonoscopy at timepoint 3 to assess for complete mucosal healing (CMH), defined as Mayo-score or SES-CD of 0 in UC and CD respectively. Results 357 qFIT results were analysed from 124 patients (58 colonic CD/ 66 UC). qFIT was significantly higher in active disease compared to remission (n=227 vs. 130; 100µgHb/g vs 2.5 µgHb/g; p<0.001). The ability of qFIT to predict active disease was higher in those with UC than colonic CD AUC 0.77 and 0.69 respectively (in 179 UC /178 CD samples). Optimal threshold for predicting remission was <14µgHb/g, with a sensitivity of 0.73, specificity of 0.66. 27 qFIT results from ileal CD patients showed no significant difference between active disease and remission (p=0.2). 339 qFIT results had paired FCal results. The ability of fCal to predict active disease was higher in UC than colonic CD with AUC of 0.82 and 0.77 respectively. For both IBD types, the AUC was 0.81 [Figure 1]. The optimal fCal level was 240 µg/g for remission, with a sensitivity of 0.76 and specificity of 0.72. Combining qFIT and fCal improved prediction of remission to an AUC of 0.82. qFIT performed better than fCal at predicting mucosal inflammation with an AUC 0.74 vs. 0.67 respectively. In 76 UC patients, Mayo endoscopy scores were paired with mean qFIT levels, showing a correlation: score 0 = 32 µgHb/g, score 1 = 181 µgHb/g, score 2 = 217 µgHb/g, and score 3 = 295 µgHb/g (one-way ANOVA, p = <0.001). 20 patients (from 52) achieving CMH, showed significant reduction in qFIT using paired T-test (107 vs 37 µgHb/g, p=0.026). In the 32 patients with ongoing inflammation, qFIT remained high (213 vs. 170 µgHb/g, p=0.23). Conclusion qFIT is comparable to fCal as a biomarker of IBD activity. With its superior patient acceptability and intrinsic test qualities, it should be considered as routine clinical test in IBD. Further large scale validation is warranted to establish its clinical utility. References *AUC – Area Under Curve **HBI-Harvey Bradshaw Index ***SCCAI – Simple Colitis Activity Index

P0250 Circulating blood matrix metalloproteinase (MMP)-10 and 12 can accurately predict mucosal healing in Inflammatory Bowel Disease (IBD) in a multi-centred prospective longitudinal cohort study (2020-2024)

Abstract Background Complete mucosal healing (CMH) is a key prognostic indicator of long-term remission in IBD. There are however, no early biomarkers of mucosal healing to allow for deeper biological studies and potential for treatment stratification. Here, we prospectively investigate the clinical utility of a novel custom-designed 21-proteomic PEA-Olink* Flex panel in blood to identify proteins with the propensity for mucosal healing. Methods In the multi-centre, prospective, longitudinal IBD study (www.musicstudy.uk), we used a custom selected 21-protein marker panel (based on roles in resolution of inflammation; Olink Flex [Figure 1]) in blood plasma (1µl), collected at timepoints T1, T2 and T3 (months 0, +3 and +6 respectively). This uses a proximity extension assay, allowing for highly specific and sensitive absolute protein quantification. All subjects had prospective endoscopic assessment T1→T2→T3 (follow-up mean 5.2 months). CMH was defined by a Mayo score of 0 in UC and SES-CD of 0 in CD. Statistical analysis was performed using R, adjusting for age and gender. A machine learning (ML) model using SHAP (SHapley Additive exPlanations) analysis was then applied to the data. Results A total of 239 blood samples from 81 IBD patients were analysed (median 37 years, 59% male; 37% UC) – all with paired endoscopic mucosal healing data. 27/81 patients (33%) achieved CMH at follow-up. Of 21 blood proteins, multivariate analysis showed MMP10 and MMP12 concentrations to be significantly associated with CMH at timepoint 3 (FDR p=0.02 for both) with AUC 0.68 and 0.64 respectively; comparable to faecal calprotectin (AUC 0.69) in predicting mucosal healing. Both proteins were significantly lower in the UC CMH group vs no CMH (p=0.01 respectively), with only MMP10 (p=0.02) being associated with CMH in CD alone. In UC, endoscopic severity score UCEIS at follow-up endoscopy, correlated strongly with MMP10 and MMP12 levels combined (p<0.001, r=0.62). In UC, ΔMMP10 levels over time were highly significant in predicting improvement in endoscopic scores; but not CD. Random Forest ML model (incorporating 80 clinical variables including drug treatment) to the 21 proteins from T1→T2→T3 predicted CMH with an AUC of 0.69. SHAP analysis identified MMP12 in the top 3 variables impacting the model’s prediction. Conclusion Our study identifies tissue remodelling proteins MMP-10 and 12 as biomarkers for CMH on par with faecal calprotectin. The ΔMMP10 expression in UC shows dynamic change, predicting mucosal healing with more significance over time. MMP-10 and 12 are involved in the tissue degradation and remodelling process of inflamed mucosa which may explain the reduction in blood levels as mucosal inflammation resolves. Further large-scale validation work is planned. References *PEA-Olink: Proximity Extension Assay

Indomethacin and Cefazolin for Physical Exam-Indicated Cerclage to Improve Latency: A Protocol Validation Study.

A single-center randomized trial showed improved latency with use of indomethacin and cefazolin (I/C) during and following exam-indicated cerclage (EIC). The same center recently published a pre/post comparison demonstrating similar results. This research aimed to validate the protocol in a different setting. Retrospective cohort study of singleton pregnancies undergoing EIC at a single center between 2013 and 2022. EIC was performed for painless cervical dilation between 16 and 23 weeks' gestation with dilation ≥1 cm. Exposure was defined as receipt of I/C during and following EIC. The primary outcome was latency ≥ 28 days after cerclage placement. Secondary outcomes included latency; gestational age at delivery; delivery ≤ 28 weeks; preterm premature rupture of membranes; intra-amniotic infection (IAI); and median birth weight. Bivariate statistics were used to analyze data; multivariable regression analyses were used to control for confounders (progesterone use, cervical dilation at time of cerclage placement, history of preterm birth, and prolapsing membranes). EIC was placed in 81 pregnancies and 48 (59%) received I/C. Baseline characteristics did not differ between groups, except that prolapsing membranes were significantly less likely in patients receiving I/C (6.2 vs. 21.2%; p = 0.04). Latency ≥ 28 days occurred in 90% of I/C recipients and 82% of the controls (p = 0.32); this difference remained nonsignificant after controlling for confounders (adjusted relative risk: 1.02 [95% confidence interval: 0.85, 1.21]). I/C recipients had lower rates of IAI (4.2 vs. 24.2%, p = 0.007), even after adjustment (adjusted relative risk: 0.18 [95% confidence interval: 0.04, 0.74]). Other secondary outcomes did not differ. Use of I/C at the time of EIC at this center was not associated with increased latency to delivery (albeit in a small cohort) but was associated with lower rates of intra-amniotic infection. Larger-scale validation studies would be helpful to confirm the value of this intervention. · Indomethacin/cefazolin (I/C) increased latency for exam-indicated cerclage in a trial.. · We performed an observational comparison to validate these findings.. · I/C use for exam-indicated cerclages was associated with decreased intra-amniotic infection.. · I/C was not associated with change in latency.. · Larger-scale validation studies needed to confirm the value of intervention..

Multimodal imaging prediction models for preoperative microvascular invasion in hepatocellular carcinoma: A systematic review and predictive accuracy analysis from biomechanical perspective

This meta-analysis aimed to evaluate the accuracy of multimodal imaging prediction models for preoperative microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients from both radiological and biomechanical perspectives. We systematically searched PubMed, Embase, and Cochrane Library databases, including 42 studies with 10,876 patients. Statistical analysis using a bivariate random-effects model assessed the diagnostic performance of different imaging modalities and prediction model types, with particular emphasis on biomechanical features including tissue elasticity, vascular wall mechanics, and tumor microenvironment properties. Results demonstrated excellent performance of multimodal imaging prediction models incorporating biomechanical parameters in MVI prediction, with a pooled sensitivity of 0.78 (95% CI: 0.73–0.82), specificity of 0.80 (95% CI: 0.76–0.84), and area under the curve (AUC) of 0.86 (95% CI: 0.83–0.89). Deep learning approaches demonstrated particular advantages in feature extraction and biomechanical pattern recognition, achieving superior performance (AUC 0.88) through their ability to automatically learn hierarchical representations from complex imaging data and mechanical data. The integration of multiple imaging modalities with biomechanical parameters further enhanced predictive accuracy (AUC 0.91), offering complementary information that captures different aspects of tumor biology, mechanics and behavior. This enhanced performance of multimodal combinations, particularly when leveraging biomechanical features and deep learning algorithms, suggests significant potential for improving clinical decision-making and treatment planning in HCC patients. Future research should focus on large-scale prospective validation, standardization of biomechanical measurements, and clinical application assessment to further enhance the accuracy and clinical value of MVI prediction.

LungDiag: Empowering artificial intelligence for respiratory diseases diagnosis based on electronic health records, a multicenter study.

Respiratory diseases pose a significant global health burden, with challenges in early and accurate diagnosis due to overlapping clinical symptoms, which often leads to misdiagnosis or delayed treatment. To address this issue, we developed LungDiag, an artificial intelligence (AI)-based diagnostic system that utilizes natural language processing (NLP) to extract key clinical features from electronic health records (EHRs) for the accurate classification of respiratory diseases. This study employed a large cohort of 31,267 EHRs from multiple centers for model training and internal testing. Additionally, prospective real-world validation was conducted using 1142 EHRs from three external centers. LungDiag demonstrated superior diagnostic performance, achieving an F1 score of 0.711 for top 1 diagnosis and 0.927 for top 3 diagnoses. In real-world testing, LungDiag outperformed both human experts and ChatGPT 4.0, achieving an F1 score of 0.651 for top 1 diagnosis. The study emphasizes the potential of LungDiag as an effective tool to support physicians in diagnosing respiratory diseases more accurately and efficiently. Despite the promising results, further large-scale multicenter validation with larger sample sizes is still needed to confirm its clinical utility and generalizability.

Large-Scale Experimental Validation of Real-Time Monitoring in Underground Gas Storage Wells Using Distributed Fiber Optic Sensing

Large-Scale Experimental Validation of Real-Time Monitoring in Underground Gas Storage Wells Using Distributed Fiber Optic Sensing

Large-Scale Validation of the Feasibility of GPT-4 as a Proofreading Tool for Head CT Reports.

Background The increasing workload of radiologists can lead to burnout and errors in radiology reports. Large language models, such as OpenAI's GPT-4, hold promise as error revision tools for radiology. Purpose To test the feasibility of GPT-4 use by determining its error detection, reasoning, and revision performance on head CT reports with varying error types and to validate its clinical utility by comparison with human readers. Materials and Methods A total of 10 300 head CT reports were retrospectively extracted from the Medical Information Mart for Intensive Care III public dataset. In experiment 1, among the 300 unaltered reports and 300 versions with applied errors, GPT-4 optimization was initially conducted with 200 reports. The remaining 400 were used for evaluation of error type detection, reasoning, and revision, as well as the analysis of reports with undetected errors. The performance was also compared with that of human readers. In experiment 2, the detection performance of GPT-4 was validated on 10 000 unaltered reports that were deemed error-free by physicians, and an analysis of false-positive results was conducted. A permutation test was conducted to assess differences in performance. Results GPT-4 demonstrated commendable performance in error detection (sensitivity, 84% for interpretive error and 89% for factual error), reasoning, and revision. Compared with GPT-4, human readers had worse factual error detection sensitivity (0.33-0.69 vs 0.89; P = .008 for radiologist 4, P < .001 for others) and took longer to review (82-121 seconds vs 16 seconds, P < .001). In 10 000 reports, GPT-4 detected 96 errors, with a low positive predictive value of 0.05, yet 14% of the false-positive responses were potentially beneficial. Conclusion GPT-4 effectively detects, reasons, and revises errors in radiology reports. While it shows excellent performance in identifying factual errors, its ability to prioritize clinically significant findings is limited. Recognizing its strengths and limitations, GPT-4 could serve as a feasible tool. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Choi in this issue.

The transformative role of artificial intelligence in endoscopy

Artificial intelligence (AI) is transforming healthcare, with endoscopy emerging as a key area for its application. AI-driven tools are advancing gastrointestinal diagnostics by significantly improving the accuracy and efficiency of detecting conditions such as colorectal cancer, inflammatory bowel disease, and gastrointestinal bleeding. Notably, real-time AI-powered polyp detection has shown significant promise in reducing missed diagnoses, particularly for flat or subtle lesions. Furthermore, AI algorithms excel in lesion characterization, aiding in clinical decision-making and reducing the need for unnecessary biopsies. A major advantage of AI lies in its ability to mitigate variability in diagnostic performance, supporting less experienced endoscopists and contributing to standardized care across diverse clinical settings. Despite these advancements, challenges persist, including the need for large-scale validation of AI models, ensuring their generalizability across populations, addressing ethical and privacy concerns, and mitigating the risk of over-reliance on AI at the expense of human expertise. This perspective explores the transformative potential of AI in endoscopy, emphasizing the importance of thoughtful implementation, ethical considerations, and continued innovation to optimize its integration into clinical practice.

AI allows pre-screening of FGFR3 mutational status using routine histology slides of muscle-invasive bladder cancer

Pathogenic activating mutations in the fibroblast growth factor receptor 3 (FGFR3) drive disease maintenance and progression in urothelial cancer. 10–15% of muscle-invasive and metastatic urothelial cancer (MIBC/mUC) are FGFR3-mutant. Selective targeting of FGFR3 hotspot mutations with tyrosine kinase inhibitors (e.g., erdafitinib) is approved for mUC and requires FGFR3 mutational testing. However, current testing assays (polymerase chain reaction or next-generation sequencing) necessitate high tissue quality, have long turnover time, and are expensive. To overcome these limitations, we develop a deep-learning model that detects FGFR3 mutations using routine hematoxylin-eosin slides. Encompassing 1222 cases, our study is a large-scale validation of a model prescreening FGFR3 mutations for MIBC and mUC patients. In this work, we demonstrate that our model achieves high sensitivity (>93%) on advanced and metastatic cases while reducing molecular testing by 40% on average, thereby offering a cost-effective and rapid pre-screening tool for identifying patients eligible for FGFR3 targeted therapies.

Comparison of GastroPanel® and GENEDIA® in Diagnosing Helicobacter pylori Infection and Gastric Lesions.

Serological tests for Helicobacter pylori needs local validation as the diagnostic accuracy may vary depending on the prevalence of H. pylori. This study examined the diagnostic performance of two ELISA, GastroPanel® (GastroPanel ELISA; Biohit Oyj) and GENEDIA® (GENEDIA® H. pylori ELISA, Green Cross Co.) in Korean population. One thousand seventy seven patients who visited for esophagogastroduodenoscopy between 2013 and 2023 were prospectively enrolled, and serum samples from the subjects were tested using both GastroPanel® and GENEDIA®. The two tests were compared for their diagnostic accuracy in detecting atrophic gastritis (AG), intestinal metaplasia (IM), gastric adenoma (GA), and gastric cancer (GC), and the positivity rates by age and sex were observed. There was substantial correlation (Pearson coefficient [r] = 0.512, P < 0.001) and agreement (Cohen's Kappa coefficient [κ] = 0.723, P < 0.001) between the results obtained using GastroPanel® and GENEDIA®. The test results from the two kits did not match perfectly with a discrepancy observed in approximately 16% of cases, that 67 subjects were positive only on GENEDIA® while 75 subjects were positive only on GastroPanel®. The area under receiver operating characteristic curve for AG, IM, GA, and GC using GastroPanel® were 0.666, 0.635, 0.540, and 0.575, while the results tested using GENEDIA® were 0.649, 0.604, 0.553, and 0.555, respectively, without significant difference between the two results. GastroPanel® and GENEDIA® showed similar performance in terms of diagnostic accuracy; but the test results did not match perfectly. A large-scale validation study in Koreans is needed.

USO DE ALGORITMOS PARA DIAGNÓSTICO PRECOCE DE ALZHEIMER, PARKINSON E ESCLEROSE MÚLTIPLA

The application of artificial intelligence (AI) algorithms in the early diagnosis of neurodegenerative diseases, such as Alzheimer's, Parkinson's, and multiple sclerosis, has made significant advances. This integrative review analyzed 22 studies published between 2013 and 2023, using databases such as PubMed, IEEE Xplore, and Scopus. The results highlight the use of Convolutional Neural Networks (CNNs) in neuroimaging, such as magnetic resonance imaging and PET scans, with an accuracy of over 90% in diagnosing Alzheimer's. Multimodal approaches that integrate clinical and genetic data have demonstrated increasing efficacy. For Parkinson's, algorithms that analyze vocal signals and tremors have a sensitivity between 85% and 92%, while deep learning tools allow the detection of minimal motor changes. In the case of multiple sclerosis, models that combine magnetic resonance imaging and immunological profiles show high accuracy in the early detection of brain lesions. Despite the advances, challenges persist, including the standardization of databases, large-scale validation, and interpretation of results by health professionals. The limitations of this study include the lack of methodological uniformity in the articles analyzed and the scarcity of data from large clinical studies. It is proposed that future research invest in the integration of different data sources, expansion of population samples, and development of more transparent algorithms, facilitating its clinical adoption. It is concluded that AI has great potential to transform early diagnosis, allowing more effective and personalized interventions, but it requires refinement to consolidate its practical applicability.

Leveraging Saliva for Insights into Head and Neck Cancer

Head and neck cancer (HNC) represents a heterogeneous group of malignancies with increasing global incidence and notable mortality. Early detection is essential for improving survival rates and minimizing recurrence; however, existing diagnostic methods are often invasive and complex. There is a need for noninvasive and more effective approaches for early detection and real-time monitoring of HNC. Saliva contains various biomolecules that may serve as indicators of HNC. As a result, saliva-based biomarkers have emerged as a transformative approach in the diagnosis and treatment of HNC due to their ease of collection, non-invasiveness, and potential to provide details about biomolecular changes associated with cancer progression. This narrative review synthesizes the current literature on the potential of saliva as a noninvasive diagnostic tool for HNC. It highlights various biomarkers found in saliva, including cell-free DNA, RNA, proteins, and metabolites, and explores emerging technologies in saliva detection that could transform the future of HNC management. Continued research efforts and larger-scale validation studies are essential to fully realize the potential of saliva-based biopsy and help pinpoint notable biomarkers to improve patient outcomes and reduce mortality associated with HNC worldwide.

Leveraging the Thermodynamics of Protein Conformations in Drug Discovery.

As the name implies, structure-based drug design requires confidence in the holo complex structure. The ability to clarify which protein conformation to use when ambiguity arises would be incredibly useful. We present a large scale validation of the computational method Protein Reorganization Free Energy Perturbation (PReorg-FEP) and demonstrate its quantitative accuracy in selecting the correct protein conformation among candidate models in apo or ligand induced states for 14 different systems. These candidate conformations are pulled from various drug discovery related campaigns: cryptic conformations induced by novel hits in lead identification, binding site rearrangement during lead optimization, and conflicting structural biology models. We also show an example of a pH-dependent conformational change, relevant to protein design.

Exploring and Predicting HIV Preexposure Prophylaxis Adherence Patterns Among Men Who Have Sex With Men: Randomized Controlled Longitudinal Study of an mHealth Intervention in Western China.

Preexposure prophylaxis (PrEP) is an effective strategy to reduce the risk of HIV infection. However, the efficacy of PrEP is highly dependent on adherence. Meanwhile, adherence changes over time, making it difficult to manage effectively. Our study aimed to explore and predict the patterns of change in PrEP adherence among men who have sex with men (MSM) and evaluate the impact of the WeChat-based reminder intervention on adherence, thus providing more information for PrEP implementation strategies. From November 2019 to June 2023, in a randomized controlled longitudinal study of the PrEP demonstration project in Western China (Chongqing, Sichuan, and Xinjiang) based on a mobile health (mHealth) reminder intervention, participants were randomly divided into reminder and no-reminder groups, with those in the reminder group receiving daily reminders based on the WeChat app. Participants were followed up and self-reported their medication adherence every 12 weeks for a total of 5 follow-up visits. We used the growth mixture model (GMM) to explore potential categories and longitudinal trajectories of adherence among MSM, and patterns of change in PrEP adherence were predicted and evaluated based on the decision tree. A total of 446 MSM were included in the analysis. The GMM identified 3 trajectories of adherence: intermediate adherence group (n=34, 7.62%), low adherence ascending group (n=126, 28.25%), and high adherence decline group (n=286, 64.13%). We included 8 variables that were significant in the univariate analysis in the decision tree prediction model. We found 4 factors and 8 prediction rules, and the results showed that HIV knowledge score, education attainment, mHealth intervention, and HIV testing were key nodes in the patterns of change in adherence. After 10-fold cross-validation, the final prediction model had an accuracy of 75%, and the classification accuracy of low and intermediate adherence was 78.12%. The WeChat-based reminder intervention was beneficial for adherence. A short set of questions and prediction rules, which can be applied in future large-scale validation studies, aimed at developing and validating a short adherence assessment tool and implementing it in PrEP practices among MSM.

The Role of LncRNA ROR in Colorectal Cancer Diagnosis and Treatment

Context: Colorectal cancer (CRC) is one of the most common causes of cancer-related deaths worldwide, particularly in developed countries. Despite advances in detection and treatment, CRC remains difficult to diagnose in its early stages, complicating patient outcomes. Recently, long non-coding RNAs (lncRNAs) have emerged as significant regulators in cancer biology, offering new opportunities for cancer diagnostics. Evidence Acquisition: This review highlights the molecular mechanisms through which LncRNA ROR influences CRC development, its diagnostic potential as a biomarker, and the future challenges of integrating LncRNA ROR into clinical practice. Results: Future research must focus on large-scale validation studies and explore the therapeutic implications of targeting LncRNA ROR. Conclusions: Overall, this review positions LncRNA ROR as a promising biomarker with potential applications in both CRC diagnosis and treatment.

Proteomics and Metabolomics in Varicocele-Associated Male Infertility: Advancing Precision Diagnostics and Therapy.

Background/Objectives: Varicoceles are a common contributor to male infertility, significantly impacting male-factor infertility cases. Traditional diagnostic methods often lack the sensitivity to detect the molecular and cellular disruptions caused by varicoceles, limiting the development of effective, personalized treatments. This narrative review aims to explore the advancements in proteomics and metabolomics as innovative, non-invasive diagnostic tools for varicocele-associated male infertility and their potential in guiding personalized therapeutic strategies. Methods: A comprehensive literature search was conducted using databases such as PubMed, Scopus, and Web of Science up to October 2024. Studies focusing on the application of proteomic and metabolomic analyses in varicocele-associated male infertility were selected. The findings were critically analyzed to synthesize current knowledge and identify future research directions. Results: Proteomic analyses revealed differentially expressed proteins in the sperm and seminal plasma of varicocele patients, revealing disruptions in pathways related to oxidative stress, mitochondrial dysfunction, apoptosis, and energy metabolism. Key proteins such as heat shock proteins, mitochondrial enzymes, and apoptotic regulators were notably altered. Metabolomic profiling uncovered specific metabolites in seminal plasma-such as decreased levels of lysine, valine, and fructose-that correlate with impaired sperm function and fertility potential. The integration of proteomic and metabolomic data provides a comprehensive molecular fingerprint of varicocele-induced infertility, facilitating the identification of novel biomarkers for early diagnosis and the development of personalized therapeutic interventions. Conclusions: Advances in proteomics and metabolomics have significantly enhanced our understanding of the molecular mechanisms underlying varicocele-associated male infertility. These "omics" technologies hold great promise for improving diagnostic accuracy and personalizing treatment, ultimately leading to better outcomes for affected men. Future large-scale clinical trials and validations are essential to confirm these biomarkers and facilitate their integration into routine clinical practice.

Applied body-fluid analysis by wearable devices.

Wearable sensors are a recent paradigm in healthcare, enabling continuous, decentralized, and non- or minimally invasive monitoring of health and disease. Continuous measurements yield information-rich time series of physiological data that are holistic and clinically meaningful. Although most wearable sensors were initially restricted to biophysical measurements, the next generation of wearable devices is now emerging that enable biochemical monitoring of both small and large molecules in a variety of body fluids, such as sweat, breath, saliva, tears and interstitial fluid. Rapidly evolving data analysis and decision-making technologies through artificial intelligence has accelerated the application of wearables around the world. Although recent pilot trials have demonstrated the clinical applicability of these wearable devices, their widespread adoption will require large-scale validation across various conditions, ethical consideration and sociocultural acceptance. Successful translation of wearable devices from laboratory prototypes into clinical tools will further require a comprehensive transitional environment involving all stakeholders. The wearable device platforms must gain acceptance among different user groups, add clinical value for various medical indications, be eligible for reimbursements and contribute to public health initiatives. In this Perspective, we review state-of-the-art wearable devices for body-fluid analysis and their translation into clinical applications, and provide insight into their clinical purpose.