Tag: nevin manimala

BMC Pulm Med. 2025 Apr 14;25(1):182. doi: 10.1186/s12890-025-03654-5.

ABSTRACT

BACKGROUND: Surfactant decreases surface tension in the peripheral airways and plays a role in regulating the lung’s immune responses. Several reports have documented changes in surfactant proteins levels, especially surfactant protein D (SP-D) and surfactant protein A (SP-A), suggesting their potential as biomarkers for asthma. However, the results of these studies are controversial. This systematic review was done to assess the levels of surfactant proteins in asthmatic patients compared to healthy individuals.

METHODS: A systematic review was conducted according to PRISMA guidelines. Searches were performed in the Medline/PubMed, Web of Science, Embase, and ScienceDirect databases to identify studies that assessed surfactants proteins levels in asthmatic patients. Pooled standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated using R version 4.4.3 meta package.

RESULTS: A total of 16 studies met the inclusion criteria and were thus considered for this systematic review. Among these, SP-D was the most frequently studied protein in relation to asthma, asthma severity, and lung function parameters in asthmatic patients. Serum and sputum levels of SP-D in asthmatic patients were slightly elevated compared to non-asthmatic individuals. However, these differences were not statistically significant; the pooled SMDs were 0.27 (95% CI: -0.034 to 0.574, P = 0.082) for serum levels and 1.47 (95% CI, -0.197 to 3.103, P = 0.084) for sputum levels. Similarly, no significant difference was detected for the analysis of serum SP-A levels, with SMD = 0.18 (95% CI, -0.505 to 0.866, P = 0.606). Though, some of the reviewed studies showed an association between SP-D levels and disease severity in asthmatic patients.

CONCLUSION: Although alterations have been observed in asthma and proposed as biomarkers, this systematic review did not find significant differences in the levels between asthmatics and healthy individuals. However, some studies have suggested an association between SP-D levels and asthma severity. Given the limited number of studies investigating this association, further research is needed to validate the clinical relevance of correlation between SP-D levels and asthma severity.

PMID:40229817 | DOI:10.1186/s12890-025-03654-5

Radiography (Lond). 2025 Apr 13;31(3):102948. doi: 10.1016/j.radi.2025.102948. Online ahead of print.

ABSTRACT

INTRODUCTION: Integrating decision support mechanisms utilising artificial intelligence (AI) into medical radiation practice introduces unique challenges to accountability for patient care outcomes. AI systems, often seen as “black boxes,” can obscure decision-making processes, raising concerns about practitioner responsibility, especially in adverse outcomes. This study examines how medical radiation practitioners perceive and attribute moral responsibility when interacting with AI-assisted decision-making tools.

METHODS: A cross-sectional online survey was conducted from September to December 2024, targeting international medical radiation practitioners. Participants were randomly assigned one of four profession-specific scenarios involving AI recommendations and patient outcomes. A 5-point Likert scale assessed the practitioner’s perceptions of moral responsibility, and the responses were analysed using descriptive statistics, Kruskal-Wallis tests, and ordinal regression. Demographic and contextual factors were also evaluated.

RESULTS: 649 radiographers, radiation therapists, nuclear medicine scientists, and sonographers provided complete responses. Most participants (49.8 %) had experience using AI in their current roles. Practitioners assigned higher moral responsibility to themselves in positive patient outcomes compared to negative ones (χ²(1) = 18.98, p < 0.001). Prior knowledge of AI ethics and professional discipline significantly influenced responsibility ratings. While practitioners generally accepted responsibility, 33 % also attributed shared responsibility to AI developers and institutions.

CONCLUSION: Patient outcomes significantly influence perceptions of moral responsibility, with a shift toward shared accountability in adverse scenarios. Prior knowledge of AI ethics is crucial in shaping these perceptions, highlighting the need for targeted education.

IMPLICATIONS FOR PRACTICE: Understanding practitioner perceptions of accountability is critical for developing ethical frameworks, training programs, and shared responsibility models that ensure the safe integration of AI into clinical practice. Robust regulatory structures are necessary to address the unique challenges of AI-assisted decision-making.

PMID:40228324 | DOI:10.1016/j.radi.2025.102948

Eur J Radiol. 2025 Apr 10;187:112106. doi: 10.1016/j.ejrad.2025.112106. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aims to investigate the value of a radiomics nomogram based on magnetic resonance imaging (MRI) in distinguishing vertebral compression fractures (VCFs) from vertebral osteomyelitis (VOs).

MATERIALS AND METHODS: We conducted a retrospective analysis of the clinical data from 100 patients with VCFs and VOs, respectively at our hospital. The cases were randomly divided into training (n = 140) and testing sets (n = 60) in a 7:3 ratio. Two experienced radiologists outlined the regions of interest (ROI) on the MRI images using T2-weighted fat suppression (T2WI-FS) images and extracted the radiomic features. The Least Absolute Shrinkage and Selection Operator (Lasso) algorithm was used to select and reduce radiomic features to establish a radiomics model (Model 1), and a Logistic Regression algorithm was used to construct a radiomics score. A multivariable logistic regression analysis was conducted to establish a clinical model (Model 2). A combined model (radiomics nomogram, Model 3) was built based on the radiomics score and independent clinical factors. The diagnostic performance of Models 1, 2, and 3 was validated using the Area Under the Curve (AUC) and Decision Curve Analysis (DCA).

RESULTS: The training and testing sets included 68/72 VCFs and 32/28 patients with VOs, respectively. There were no statistically significant differences in clinical characteristics such as age, sex, body mass index (BMI), CRP levels, ESR, and lesion stage between the training and testing sets (P > 0.05). A total of 873 radiomic features and 6 clinical features were extracted. After screening, 10 optimal features were selected to build Model 1, while 5 clinical features were used to build Model 2. Models 1 and 2 were combined to create Model 3 and a nomogram was plotted. All the three models were constructed using Logistic Regression algorithms. Model 3 achieved a higher AUC than Models 1 and 2 for both the training and testing sets: 0.946 > 0.904 > 0.871 (training) and 0.900 > 0.854 > 0.818 (testing), respectively. Additionally, the DCA indicated that Model 3 had better clinical utility than Models 1 and 2.

CONCLUSION: Our analysis indicated that the radiomics nomogram, combined with radiomic and clinical features, provides significant clinical guidance in distinguishing vertebral compression fractures from spinal vertebral osteomyelitis.

PMID:40228322 | DOI:10.1016/j.ejrad.2025.112106

J Cosmet Laser Ther. 2025 Apr 14:1-7. doi: 10.1080/14764172.2025.2468499. Online ahead of print.

ABSTRACT

Androgenetic alopecia significantly affects individuals’ quality of life, creating a need for better therapeutic strategies. Hair transplant surgery plays a key role in treating hair loss, with adjuncts like PRP and meso-cocktails explored to enhance results and procedure longevity. A prospective, single-blind study involved 112 males aged 25-50 years with Norwood-Hamilton grade IV-VI alopecia. Patients were randomly divided into two groups receiving different transplant methods. In Stage I, hair condition was evaluated, and QR678 Neo® was administered intradermally in Group A. Stage II involved hair transplantation surgery with diluted QR678 Neo® for graft preservation. Post-transplantation (Stage III) included additional QR678 Neo® administration in Group A. Evaluation methods consisted of global photographic assessment and videomicroscopy to measure hair growth and density. Group A (QR678 Neo®) demonstrated significant improvements in regrowth, density, and shaft diameter compared to Group B (conventional transplant). By Stage III, Group A had higher Global Photographic Assessment scores (8.87) and terminal hair count (181.02), with statistically significant differences (p < .001). The study suggests that QR678 Neo® improves outcomes and offers a promising treatment option for androgenetic alopecia.

PMID:40228316 | DOI:10.1080/14764172.2025.2468499

JMIR Rehabil Assist Technol. 2025 Apr 14;12:e66352. doi: 10.2196/66352.

ABSTRACT

BACKGROUND: Chronic pain in adolescents is a significant and growing concern, as it can have negative implications on physical and psychosocial development. Management can be complicated by the increasing risks associated with opioid misuse, highlighting the need for effective nonpharmacological interventions. Biofeedback is an empirically supported behavioral intervention for chronic pain that targets the self-regulation of physiological responses. Virtual reality (VR) is a novel delivery method for biofeedback that could serve as an engaging and effective platform for adolescents.

OBJECTIVE: The goal of this study was to assess the feasibility, acceptability, and preliminary effectiveness of integrating a VR-delivered respiratory biofeedback intervention into an outpatient pediatric pain rehabilitation program (PPRP) for adolescents with chronic pain.

METHODS: In this pilot study, we recruited 9 participants from those enrolled in the PPRP at Nemours Children’s Hospital. Participants underwent 2 VR respiratory biofeedback sessions per week over a 4-week period using AppliedVR’s “RelieVRx” program. Feasibility was defined as >60% of eligible patients enrolling with at least 80% of VR sessions completed. Acceptability was assessed via validated acceptability questionnaires, with high acceptability defined as an average acceptability rating score >3 on a 5-point Likert scale. Open-ended responses were analyzed via qualitative analysis. Preliminary effectiveness was assessed with questionnaires measuring the quality of life (Pediatric Quality of Life Inventory [PedsQL]) and level of pain interference in daily activities (Functional Disability Inventory) before and after participation in the pain program. Finally, heart rate (HR) and blood pressure (BP) were measured before and after each VR session.

RESULTS: Of 14 eligible PPRP patients, 9 (64%) enrolled in the VR respiratory biofeedback study, and 7 (77% of study participants) completed at least 80% of biofeedback sessions. Participants reported high acceptability with average session ratings ranging from 3.89 to 4.16 on post-VR program questionnaires. Of 224 open-ended responses, participants reported changes in stress and somatic symptoms (ie, pain distraction and breathing regulation). There was a statistically significant increase in the average physical functioning score of the PedsQL among participants (P=.01) from pre- to postparticipation in the overall pain program. The cohort’s average emotional functioning score of the PedsQL also increased, though this change was not statistically significant (P=.17). Participants’ Functional Disability Inventory scores significantly decreased from an average of 25.1 to 11 from before to after the pain program (P=.002). There were no significant differences between pre- versus post-BP or HR for any session. However, decreased BP and HR were observed across most sessions.

CONCLUSIONS: AppliedVR respiratory biofeedback demonstrated initial feasibility, acceptability, and preliminary effectiveness when implemented as part of a PRPP. This study underscores the need for future larger-scale studies analyzing the use of VR biofeedback in adolescent populations with chronic pain.

PMID:40228293 | DOI:10.2196/66352

JMIR Public Health Surveill. 2025 Apr 14;11:e66062. doi: 10.2196/66062.

ABSTRACT

BACKGROUND: GPFirst is a primary care partnership program designed to encourage patients with nonurgent conditions to seek care at participating general practitioner clinics instead of visiting the emergency department. In 2019, a digital media campaign (DMC) was launched to raise awareness and knowledge about GPFirst among residents in eastern Singapore.

OBJECTIVE: This study aims to assess the DMC’s impact on awareness and knowledge of GPFirst across different age groups, and the acceptability and satisfaction of GPFirst.

METHODS: The DMC, comprising Facebook posts and a website designed using the Andersen behavioral model, was evaluated through 2 repeated cross-sectional surveys. The first cross-sectional survey (CS1) was conducted with eastern Singapore residents aged 21 years and older, 2 1 year before the campaign’s launch, and the second survey (CS2) 4 months after. Satisfaction was measured on a 5-point Likert scale (very poor to excellent) about GPFirst experiences. Acceptability was assessed with 3 yes or no questions on decisions to visit or recommend GPFirst clinics. Analyses used tests of proportions, adjusted multiregression models, and age-stratified secondary analyses.

RESULTS: The Facebook posts generated 38,404 engagements within 5 months, with “#ThankYourGP” posts being the most viewed (n=24,602) and engaged (n=2618). Overall, 1191 and 1161 participants completed CS1 and CS2 respectively. Compared to CS1, CS2 participants were more aware (odds ratio [OR] 2.64, 95% CI 2.11-3.31; P<.001) and knowledgeable of GPFirst (OR 4.20, 95% CI 2.62-6.73; P<.001). Awareness was higher among married individuals (OR 1.31, 95% CI 1.04-1.66; P=.03), those without a regular primary care physician (OR 1.79, 95% CI 1.44-2.22; P<.001), and with higher education levels. Similarly, knowledge was greater among individuals with secondary (OR 2.88, 95% CI 1.35-6.17; P=.006) and preuniversity education (OR 2.56, 95% CI 1.14-5.70; P=.02), and those without a regular primary care physician (OR 1.54, 95% CI 1.02-2.34; P=.04). For acceptability, among participants who visited a GPFirst clinic, 98.2% (163/166) reported they would continue to visit a GPFirst clinic before the emergency department in the future, 95.2% (158/166) would recommend the clinic, 60.2% (100/166) cited the clinic’s participation in GPFirst as a factor in their provider’s choice and 87.3% (145/166) were satisfied with GPFirst. Among those unaware of GPFirst, 88.3% (1680/1903) would consider visiting a GPFirst clinic before the emergency department in the future.

CONCLUSIONS: The DMC improved awareness and knowledge of GPFirst, with high satisfaction and acceptability among participants. Age-dependent strategies may improve GPFirst participation. The “#ThankYourGP” campaign demonstrated the potential of user-generated content to boost social media engagement, a strategy that international health systems could adopt.

PMID:40228291 | DOI:10.2196/66062

JMIR Form Res. 2025 Apr 14;9:e66301. doi: 10.2196/66301.

ABSTRACT

BACKGROUND: Approximately 20% of US adults identify as having a mental illness. Structural and other barriers prevent many people from receiving mental health services. Digital mental health apps that provide 24-hour, real-time access to human support may improve access to mental health services. However, information is needed regarding how and why people engage with licensed counselors through a digital, real-time, text-based mental health support app in nonexperimental settings.

OBJECTIVE: This study aimed to evaluate how people engage with Counslr, a 24-hour, digital, mental health support app where users communicate in real time with human counselors through text messaging. Specifically, access patterns (eg, day of the week and time of session) and reasons for accessing the platform were examined. Furthermore, whether differences existed between session types (on-demand or scheduled) and membership types (education or noneducation) in regard to access patterns and why people accessed the platform were evaluated.

METHODS: The study population (users) consisted of students whose schools, universities, or colleges partnered with Counslr and employees whose organizations also partnered with Counslr. Users participated in text-based mental health support sessions. In these sessions, users engaged with licensed counselors through digital, text-based messaging in real time. Users could initiate an on-demand session or schedule a session 24 hours a day. User engagement patterns were evaluated through session length, session day, session time, and self-reported reasons for initiating the session. The data were stratified by membership type (education [students] or noneducation [employees]) and session type (on-demand or scheduled) to evaluate whether differences existed in usage patterns and self-reported reasons for initiating sessions by membership and session types.

RESULTS: Most students (178/283, 62.9%) and employees (28/44, 63.6%) accessed Counslr through on-demand sessions. The average and median session times were 40 (SD 15.3) and 45 minutes. On-demand sessions (37.9 minutes) were shorter (P=.001) than scheduled sessions (43.5 minutes). Most users (262/327, 80.1%) accessed Counslr between 7 PM and 5 AM. The hours that users accessed Counslr did not statistically differ by membership type (P=.19) or session type (P=.10). Primary self-reported reasons for accessing Counslr were relationship reasons, depression, and anxiety; however, users initiated sessions for a variety of reasons. Statistically significant differences existed between membership and session types (P<.05) for some of the reasons why people initiated sessions.

CONCLUSIONS: The novel findings of this study illustrate that real-time, digital mental health support apps, which offer people the opportunity to engage with licensed counselors outside of standard office hours for a variety of mental health conditions, may help address structural barriers to accessing mental health support services. Additional research is needed to evaluate the effectiveness of human-based apps such as Counslr and whether such apps can also address disparities in access to mental health support services among different demographic groups.

PMID:40228290 | DOI:10.2196/66301

Medicine (Baltimore). 2025 Apr 11;104(15):e41839. doi: 10.1097/MD.0000000000041839.

ABSTRACT

This study aimed to evaluate return to work (RTW) across different job groups, identify predictors of successful RTW, and investigate reasons for RTW failure. This case-control study, conducted in 2022, included 164 male patients who had ST elevation myocardial infarction (STEMI) in 2016 to 2017 and were registered in the 5-year ST Elevation Myocardial Infarction Cohort in Isfahan, Iran. Patients were divided into RTW (n = 82) and RTW failure (n = 82) groups, frequency-matched for education, marital status, and comorbidities. Baseline data were extracted from the cohort database, and occupational factors were gathered via telephone contact. Statistical analysis was performed using chi-square tests, t tests, and multivariate logistic regression to identify significant predictors of RTW, with P < .05 considered statistically significant. Data from 164 patients aged 18 to 65 with STEMI showed that those who returned to work had a mean age of 49.05 years, compared to 53.04 years for those who did not (P = .001). Factors associated with increased RTW included younger age (odds ratios [OR]: 0.86; 95% confidence intervals: 0.77-0.95), shorter hospitalization (OR: 0.63; 0.44-0.91), and lower first systolic blood pressure (OR: 0.97; 0.94-0.99). Most patients (49.39%, n = 59) returned within 1 month. Common RTW failure reasons were personal decisions (36.58%, n = 30), retirement (25.61%, n = 21), and choosing lighter jobs (25.61%, n = 21). No significant relationship was found between job groups of the International Standard Classification of Occupations and RTW (P = .581). Our study identifies key factors influencing RTW after STEMI, including age, history of myocardial infarction, hospitalization duration, treatment methods, and initial systolic blood pressure. The most common barrier to RTW was patient unwillingness. A comprehensive approach that integrates primary prevention, personalized rehabilitation, and financial and social support is recommended to improve RTW outcomes.

PMID:40228286 | DOI:10.1097/MD.0000000000041839

Medicine (Baltimore). 2025 Apr 11;104(15):e41918. doi: 10.1097/MD.0000000000041918.

ABSTRACT

BACKGROUND: There is a close relationship between asthma and obstructive sleep apnea (OSA), and the mechanisms of these 2 diseases are overlapped. However, the relationship between OSA and allergic asthma remains to be analyzed through systematic review and meta-analysis.

METHODS: A systematic search was conducted using Scopus, PubMed, ISI, Google Scholar, and Cochrane Library by utilizing the keywords Allergic asthma, Obstructive sleep apnea, and OSA. Hazard ratio, odds ratio (OR), and risk ratio with 95% confidence interval, fixed and Mantel-Haenszel methods were calculated. Statistical software Stata was used for the evaluation of this meta-analysis.

RESULTS: Finally, 19 articles were included in this study. The prevalence of OSA in allergic asthma patients was 35.25% (19.92%, 50.57%), which was statistically significant, and pooled analysis of ORs observed in individual studies showed that the odds of OSA prevalence were 2.24 (1.32, 3.12) (P < 0.001). Also, the prevalence of OSA risk in allergic asthma patients was 30.08% (19.73%, 40.43%), which was statistically significant, and pooled analysis of ORs observed in individual studies showed that the odds of OSA risk were 3.46 (2.96, 4.94) (P < 0.001).

CONCLUSION: The present meta-analysis showed that the prevalence of OSA as well as the OSA risk in patients with asthma were significantly higher compared with healthy people.

PMID:40228283 | DOI:10.1097/MD.0000000000041918

Medicine (Baltimore). 2025 Apr 11;104(15):e42135. doi: 10.1097/MD.0000000000042135.

ABSTRACT

Artificial intelligence (AI) and the introduction of Large Language Model (LLM) chatbots have become a common source of patient inquiry in healthcare. The quality and readability of AI-generated patient education materials (PEM) is the subject of many studies across multiple medical topics. Most demonstrate poor readability and acceptable quality. However, an area yet to be investigated is chemotherapy-induced cardiotoxicity. This study seeks to assess the quality and readability of chatbot created PEM relative to chemotherapy-induced cardiotoxicity. We conducted an observational cross-sectional study in August 2024 by asking 10 questions to 4 chatbots: ChatGPT, Microsoft Copilot (Copilot), Google Gemini (Gemini), and Meta AI (Meta). The generated material was assessed for readability using 7 tools: Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), Coleman-Liau Index (CLI), Simple Measure of Gobbledygook (SMOG) Index, Automated Readability Index (ARI), and FORCAST Grade Level. Quality was assessed using modified versions of 2 validated tools: the Patient Education Materials Assessment Tool (PEMAT), which outputs a 0% to 100% score, and DISCERN, a 1 (unsatisfactory) to 5 (highly satisfactory) scoring system. Descriptive statistics were used to evaluate performance and compare chatbots amongst each other. Mean reading grade level (RGL) across all chatbots was 13.7. Calculated RGLs for ChatGPT, Copilot, Gemini and Meta were 14.2, 14.0, 12.5, 14.2, respectively. Mean DISCERN scores across the chatbots was 4.2. DISCERN scores for ChatGPT, Copilot, Gemini, and Meta were 4.2, 4.3, 4.2, and 3.9, respectively. Median PEMAT scores for understandability and actionability were 91.7% and 75%, respectively. Understandability and actionability scores for ChatGPT, Copilot, Gemini, and Meta were 100% and 75%, 91.7% and 75%, 90.9% and 75%, and 91.7% and 50%, respectively. AI chatbots produce high quality PEM with poor readability. We do not discourage using chatbots to create PEM but recommend cautioning patients about their readability concerns. AI chatbots are not an alternative to a healthcare provider. Furthermore, there is no consensus on which chatbots create the highest quality PEM. Future studies are needed to assess the effectiveness of AI chatbots in providing PEM to patients and how the capabilities of AI chatbots are changing over time.

PMID:40228277 | DOI:10.1097/MD.0000000000042135