Effectiveness of digital tools for smoking cessation in Asian countries: a systematic review

AIM

The use of tobacco is responsible for many preventable diseases and deaths worldwide. Digital interventions have greatly improved patient health and clinical care and have proven to be effective for quitting smoking in the general population due to their flexibility and potential for personalization. However, there is limited evidence on the effectiveness of digital interventions for smoking cessation in Asian countries.

METHODS

Three major databases - Web of Science (WOS), Scopus, and PubMed - for relevant studies published between 1 January 2010 and 12 February 2023 were searched for studies evaluating the effectiveness of digital intervention for smoking cessation in Asian countries.

RESULTS

A total of 25 studies of varying designs were eligible for this study collectively involving a total of n = 22,005 participants from 9 countries. Among different digital tools for smoking cessation, the highest abstinence rate (70%) was reported with cognitive behavioural theory (CBT)-based smoking cessation intervention via Facebook followed by smartphone app (60%), WhatsApp (59.9%), and Pharmacist counselling with Quit US smartphone app (58.4%). However, WhatsApp was preferred over Facebook intervention due to lower rates of relapse. WeChat was responsible for 15.6% and 41.8% 7-day point prevalence abstinence. For telephone/text messaging abstinence rate ranged from 8-44.3% and quit rates from 6.3% to 16.8%. Whereas, no significant impact of media/multimedia messages and web-based learning on smoking cessation was observed in this study.

CONCLUSION

Based on the study findings the use of digital tools can be considered an alternative and cost-effective smoking cessation intervention as compared to traditional smoking cessation interventions.

A drug prescription recommendation system based on novel DIAKID ontology and extensive semantic rules

According to the World Health Organization (WHO) data from 2000 to 2019, the number of people living with Diabetes Mellitus and Chronic Kidney Disease (CKD) is increasing rapidly. It is observed that Diabetes Mellitus increased by 70% and ranked in the top 10 among all causes of death, while the rate of those who died from CKD increased by 63% and rose from the 13th place to the 10th place. In this work, we combined the drug dose prediction model, drug-drug interaction warnings and drugs that potassium raising (K-raising) warnings to create a novel and effective ontology-based assistive prescription recommendation system for patients having both Type-2 Diabetes Mellitus (T2DM) and CKD. Although there are several computational solutions that use ontology-based systems for treatment plans for these type of diseases, none of them combine information analysis and treatment plans prediction for T2DM and CKD. The proposed method is novel: (1) We develop a new drug-drug interaction model and drug dose ontology called DIAKID (for drugs of T2DM and CKD). (2) Using comprehensive Semantic Web Rule Language (SWRL) rules, we automatically extract the correct drug dose, K-raising drugs, and drug-drug interaction warnings based on the Glomerular Filtration Rate (GFR) value of T2DM and CKD patients. The proposed work achieves very competitive results, and this is the first time such a study conducted on both diseases. The proposed system will guide clinicians in preparing prescriptions by giving necessary warnings about drug-drug interactions and doses.

Silk-based wearable devices for health monitoring and medical treatment

Previous works have focused on enhancing the tensile properties, mechanical flexibility, biocompatibility, and biodegradability of wearable devices for real-time and continuous health management. Silk proteins, including silk fibroin (SF) and sericin, show great advantages in wearable devices due to their natural biodegradability, excellent biocompatibility, and low fabrication cost. Moreover, these silk proteins possess great potential for functionalization and are being explored as promising candidates for multifunctional wearable devices with sensory capabilities and therapeutic purposes. This review introduces current advancements in silk-based constituents used in the assembly of wearable sensors and adhesives for detecting essential physiological indicators, including metabolites in body fluids, body temperature, electrocardiogram (ECG), electromyogram (EMG), pulse, and respiration. SF and sericin play vital roles in addressing issues related to discomfort reduction, signal fidelity improvement, as well as facilitating medical treatment. These developments signify a transition from hospital-centered healthcare toward individual-centered health monitoring and on-demand therapeutic interventions.

ΔNp63 regulates MDSC survival and metabolism in triple-negative breast cancer

Triple-negative breast cancer (TNBC) contributes greatly to mortality of breast cancer, demanding new targetable options. We have shown that TNBC patients have high ΔNp63 expression in tumors. However, the function of ΔNp63 in established TNBC is yet to be explored. In current studies, targeting ΔNp63 with inducible CRISPR knockout and Histone deacetylase inhibitor Quisinostat showed that ΔNp63 is important for tumor progression and metastasis in established tumors by promoting myeloid-derived suppressor cell (MDSC) survival through tumor necrosis factor alpha. Decreasing ΔNp63 levels are associated with decreased CD4+ and FOXP3+ T-cells but increased CD8+ T-cells. RNA sequencing analysis indicates that loss of ΔNp63 alters multiple MDSC properties such as lipid metabolism, chemotaxis, migration, and neutrophil degranulation besides survival. We further demonstrated that targeting ΔNp63 sensitizes chemotherapy. Overall, we showed that ΔNp63 reprograms the MDSC-mediated immunosuppressive functions in TNBC, highlighting the benefit of targeting ΔNp63 in chemotherapy-resistant TNBC.

Research on the recognition model of exercise fatigue based on the fusion of sEMG and ECG signals

This study significantly enhances the accuracy of exercise state identification in wearable devices through improved denoising techniques for sEMG and ECG signals. By adopting an optimized Variational Mode Decomposition (VMD) method, combined with the Improved Sparrow Search Algorithm and Second Generation Wavelet Transform (ISSA-VMD-SWT), and introducing chaos mapping to strengthen the algorithm's initial population, this approach effectively reduces noise while preserving key fatigue-related features. In tests conducted on data from 32 participants, the method achieved accuracy rates of 93.25%, 95.16%, and 93.05% for identifying "Easy," "Transition," and "Tired" exercise states, respectively, showing significant advantages over traditional denoising techniques. These results indicate that the denoising technology developed in this study represents a significant technological advancement for the application of ECG and sEMG fatigue identification technologies in wearable health monitoring devices.

Prevalence, Outcomes, and Complications of Vitamin D Deficiency Among Patients with Multiple Myeloma: Nationwide Burden of Disease

Multiple myeloma (MM), constituting 10% of hematological malignancies, poses significant morbidity and mortality, especially with skeletal involvement. Bisphosphonate use in MM may lead to severe hypocalcemia due to vitamin D deficiency (VDD), exacerbating bone marrow plasma cell burden. We aimed to assess VDD prevalence and its impact on outcomes in MM patients. A retrospective cross-sectional analysis (2008-2018) of nationwide inpatient data identified adult MM hospitalizations with VDD using ICD-10-CM codes. Univariate and multivariate analyses were conducted to evaluate prevalence, demographics, and outcomes, with significance set at p < 0.05. Among 330,175 MM hospitalizations, 3.48% had VDD. VDD was more prevalent among 50-75-year-olds (61.72% vs. 59.74%), females (53.36% vs. 44.34%), Blacks (23.34% vs. 22.94%), Whites (65.84% vs. 65.79%), higher income brackets (26.13% vs. 23.85%), and those with comorbidities like hypertension (71.12% vs. 69.89%), dyslipidemia (42.47% vs. 34.98%), obesity (13.63% vs. 10.19%), and alcohol abuse (1.61% vs. 1.34%). In regression analysis, VDD in MM patients correlated with higher morbidity (aOR: 1.24, 95%CI: 1.14-1.36) and major disability (aOR: 1.26, 95%CI: 1.20-1.30). MM patients with VDD exhibit worse outcomes, underscoring the importance of recognizing and managing VDD promptly. Further prospective studies are needed to validate our findings and explore the impact of vitamin D supplementation on MM patient outcomes.

Natural language processing (NLP) to facilitate abstract review in medical research: the application of BioBERT to exploring the 20-year use of NLP in medical research

BACKGROUND

Abstract review is a time and labor-consuming step in the systematic and scoping literature review in medicine. Text mining methods, typically natural language processing (NLP), may efficiently replace manual abstract screening. This study applies NLP to a deliberately selected literature review problem, the trend of using NLP in medical research, to demonstrate the performance of this automated abstract review model.

METHODS

Scanning PubMed, Embase, PsycINFO, and CINAHL databases, we identified 22,294 with a final selection of 12,817 English abstracts published between 2000 and 2021. We invented a manual classification of medical fields, three variables, i.e., the context of use (COU), text source (TS), and primary research field (PRF). A training dataset was developed after reviewing 485 abstracts. We used a language model called Bidirectional Encoder Representations from Transformers to classify the abstracts. To evaluate the performance of the trained models, we report a micro f1-score and accuracy.

RESULTS

The trained models' micro f1-score for classifying abstracts, into three variables were 77.35% for COU, 76.24% for TS, and 85.64% for PRF. The average annual growth rate (AAGR) of the publications was 20.99% between 2000 and 2020 (72.01 articles (95% CI: 56.80-78.30) yearly increase), with 81.76% of the abstracts published between 2010 and 2020. Studies on neoplasms constituted 27.66% of the entire corpus with an AAGR of 42.41%, followed by studies on mental conditions (AAGR = 39.28%). While electronic health or medical records comprised the highest proportion of text sources (57.12%), omics databases had the highest growth among all text sources with an AAGR of 65.08%. The most common NLP application was clinical decision support (25.45%).

CONCLUSIONS

BioBERT showed an acceptable performance in the abstract review. If future research shows the high performance of this language model, it can reliably replace manual abstract reviews.

How can healthcare organisations increase doctors' research engagement? A scoping review

PURPOSE

Clinician engagement in research has positive impacts for healthcare, but is often difficult for healthcare organisations to support in light of limited resources. This scoping review aimed to describe the literature on health service-administered strategies for increasing research engagement by medical practitioners.

DESIGN/METHODOLOGY/APPROACH

Medline, EMBASE and Web of Science databases were searched from 2000 to 2021 and two independent reviewers screened each record for inclusion. Inclusion criteria were that studies sampled medically qualified clinicians; reported empirical data; investigated effectiveness of an intervention in improving research engagement and addressed interventions implemented by an individual health service/hospital.

FINDINGS

Of the 11,084 unique records, 257 studies were included. Most (78.2%) studies were conducted in the USA, and were targeted at residents (63.0%). Outcomes were measured in a variety of ways, most commonly publication-related outcomes (77.4%), though many studies used more than one outcome measure (70.4%). Pre-post (38.8%) and post-only (28.7%) study designs were the most common, while those using a contemporaneous control group were uncommon (11.5%). The most commonly reported interventions included Resident Research Programs (RRPs), protected time, mentorship and education programs. Many articles did not report key information needed for data extraction (e.g. sample size).

ORIGINALITY/VALUE

This scoping review demonstrated that, despite a large volume of research, issues like poor reporting, infrequent use of robust study designs and heterogeneous outcome measures limited application. The most compelling available evidence pointed to RRPs, protected time and mentorship as effective interventions. Further high-quality evidence is needed to guide healthcare organisations on increasing medical research engagement.

Nanomaterial-based Electrochemical Sensors for Multiplex Medicinal Applications

This review explores the advancements in nanomaterial-based electrochemical sensors for the multiplex detection of medicinal compounds. The growing demand for efficient and selective detection methods in the pharmaceutical field has prompted significant research into the development of electrochemical sensors employing nanomaterials. These materials, defined as functional materials with at least one dimension between 1 and 100 nanometers, encompass metal nanoparticles, polymers, carbon-based nanocomposites, and nano-bioprobes. These sensors are characterized by their enhanced sensitivity and selectivity, playing a crucial role in simultaneous detection and offering a comprehensive analysis of multiple medicinal complexes within a single sample. The review comprehensively examines the design, fabrication, and application of nanomaterial- based electrochemical sensors, focusing on their ability to achieve multiplex detection of various medicinal substances. Insights into the strategies and nanomaterials employed for enhancing sensor performance are discussed. Additionally, the review explores the challenges and future perspectives of this evolving field, highlighting the potential impact of nanomaterial-based electrochemical sensors on the advancement of medicinal detection technologies.

The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric analysis

Artificial intelligence (AI) has revolutionized many fields, and its potential in healthcare has been increasingly recognized. Based on diverse data sources such as imaging, laboratory tests, medical records, and electrophysiological data, diagnostic AI has witnessed rapid development in recent years. A comprehensive understanding of the development status, contributing factors, and their relationships in the application of AI to medical diagnostics is essential to further promote its use in clinical practice. In this study, we conducted a bibliometric analysis to explore the evolution of task-specific to general-purpose AI for medical diagnostics. We used the Web of Science database to search for relevant articles published between 2010 and 2023, and applied VOSviewer, the R package Bibliometrix, and CiteSpace to analyze collaborative networks and keywords. Our analysis revealed that the field of AI in medical diagnostics has experienced rapid growth in recent years, with a focus on tasks such as image analysis, disease prediction, and decision support. Collaborative networks were observed among researchers and institutions, indicating a trend of global cooperation in this field. Additionally, we identified several key factors contributing to the development of AI in medical diagnostics, including data quality, algorithm design, and computational power. Challenges to progress in the field include model explainability, robustness, and equality, which will require multi-stakeholder, interdisciplinary collaboration to tackle. Our study provides a holistic understanding of the path from task-specific, mono-modal AI toward general-purpose, multimodal AI for medical diagnostics. With the continuous improvement of AI technology and the accumulation of medical data, we believe that AI will play a greater role in medical diagnostics in the future.

Validity and reliability of an instrument evaluating the performance of intelligent chatbot: the Artificial Intelligence Performance Instrument (AIPI)

OBJECTIVES

To evaluate the reliability and validity of the Artificial Intelligence Performance Instrument (AIPI).

METHODS

Medical records of patients consulting in otolaryngology were evaluated by physicians and ChatGPT for differential diagnosis, management, and treatment. The ChatGPT performance was rated twice using AIPI within a 7-day period to assess test-retest reliability. Internal consistency was evaluated using Cronbach's α. Internal validity was evaluated by comparing the AIPI scores of the clinical cases rated by ChatGPT and 2 blinded practitioners. Convergent validity was measured by comparing the AIPI score with a modified version of the Ottawa Clinical Assessment Tool (OCAT). Interrater reliability was assessed using Kendall's tau.

RESULTS

Forty-five patients completed the evaluations (28 females). The AIPI Cronbach's alpha analysis suggested an adequate internal consistency (α = 0.754). The test-retest reliability was moderate-to-strong for items and the total score of AIPI (rs = 0.486, p = 0.001). The mean AIPI score of the senior otolaryngologist was significantly higher compared to the score of ChatGPT, supporting adequate internal validity (p = 0.001). Convergent validity reported a moderate and significant correlation between AIPI and modified OCAT (rs = 0.319; p = 0.044). The interrater reliability reported significant positive concordance between both otolaryngologists for the patient feature, diagnostic, additional examination, and treatment subscores as well as for the AIPI total score.

CONCLUSIONS

AIPI is a valid and reliable instrument in assessing the performance of ChatGPT in ear, nose and throat conditions. Future studies are needed to investigate the usefulness of AIPI in medicine and surgery, and to evaluate the psychometric properties in these fields.

Plant a seed series: the impact of an online outreach package on school pupils' knowledge, skills, and attitudes to medicine

BACKGROUND

Medicine is one of the most inaccessible professions in the United Kingdom (UK). The Plant a Seed (PAS) series was created to address this; it is an online pre-recorded three-part video series to "Inspire", "Educate" and "Motivate" pupils from widening participation backgrounds on Medicine. We explored the impact of PAS on pupils' knowledge, skills and attitude to Medicine.

METHODS

We conducted a national pretest-posttest study of Years 7-9 pupils in the UK. 503 schools were invited to PAS, following ethics approval. Consented pupils viewed all three episodes asynchronously and completed a pre-and post-series Likert scale confidence questionnaire, which evaluated their knowledge, skills and attitudes to a medical career. A Shapiro-Wilk test showed the lack of a normal distribution (p < 0.05); therefore, a Wilcoxon signed-rank test statistically compared pretest-posttest results of each pupil.

RESULTS

70 pupils in Years 7-9 from 2 schools participated in this study. PAS was shown to significantly increase pupils' knowledge of the role and life of a doctor, medicine as a degree, admissions requirements, and careers in medicine (p < 0.05). There was a significant increase in pupils believing they could study medicine. The intervention did not significantly increase the desire for pupils to study medicine (p = 0.187).

CONCLUSION

PAS significantly improved pupils' knowledge, skills and confidence demonstrating the need and benefit to enrolment of the programme at scale. It did not significantly increase the number of pupils wishing to study medicine. Analysis at scale is required to evaluate the effectiveness of PAS as a key intervention to break down barriers to medicine.

Exploring health and disease concepts in healthcare practice: an empirical philosophy of medicine study

In line with recent proposals for experimental philosophy and philosophy of science in practice, we propose that the philosophy of medicine could benefit from incorporating empirical research, just as bioethics has. In this paper, we therefore take first steps towards the development of an empirical philosophy of medicine, that includes investigating practical and moral dimensions. This qualitative study gives insight into the views and experiences of a group of various medical professionals and patient representatives regarding the conceptualization of health and disease concepts in practice and the possible problems that surround them. This includes clinical, epistemological, and ethical issues. We have conducted qualitative interviews with a broad range of participants (n = 17), working in various health-related disciplines, fields and organizations. From the interviews, we highlight several different practical functions of definitions of health and disease. Furthermore, we discuss 5 types of problematic situations that emerged from the interviews and analyze the underlying conceptual issues. By providing theoretical frameworks and conceptual tools, and by suggesting conceptual changes or adaptations, philosophers might be able to help solve some of these problems. This empirical-philosophical study contributes to a more pragmatic way of understanding the relevance of conceptualizing health and disease by connecting the participants' views and experiences to the theoretical debate. Going back and forth between theory and practice will likely result in a more complex but hopefully also better and more fruitful understanding of health and disease concepts.

Twelve tips for maximizing the potential of reflective writing in medical education

Reflective writing (RW) is a popular tool in medical education, but it is being used in ways that fail to maximize its potential. Literature in the field focuses on why RW is used - that is to develop, assess, and remediate learner competencies - but less so on how to use it effectively. The emerging literature on how to integrate RW in medical education is haphazard, scattered and, at times, reductionist. We need a synthesis to translate this literature into cohesive strategies for medical educators using RW in a variety of contexts. These 12 tips offer guidelines for the principles and practices of using RW in medical education. This synthesis aims to support more strategic and meaningful integration of RW in medical education.

Drug-drug interactions prediction based on deep learning and knowledge graph: A review

Drug-drug interactions (DDIs) can produce unpredictable pharmacological effects and lead to adverse events that have the potential to cause irreversible damage to the organism. Traditional methods to detect DDIs through biological or pharmacological analysis are time-consuming and expensive, therefore, there is an urgent need to develop computational methods to effectively predict drug-drug interactions. Currently, deep learning and knowledge graph techniques which can effectively extract features of entities have been widely utilized to develop DDI prediction methods. In this research, we aim to systematically review DDI prediction researches applying deep learning and graph knowledge. The available biomedical data and public databases related to drugs are firstly summarized in this review. Then, we discuss the existing drug-drug interactions prediction methods which have utilized deep learning and knowledge graph techniques and group them into three main classes: deep learning-based methods, knowledge graph-based methods, and methods that combine deep learning with knowledge graph. We comprehensively analyze the commonly used drug related data and various DDI prediction methods, and compare these prediction methods on benchmark datasets. Finally, we briefly discuss the challenges related to drug-drug interactions prediction, including asymmetric DDIs prediction and high-order DDI prediction.

Cross-sectional analyses of metabolites across biological samples mediating dietary acid load and chronic kidney disease

Chronic kidney disease (CKD) is a major public health burden, with dietary acid load (DAL) and gut microbiota playing crucial roles. As DAL can affect the host metabolome, potentially via the gut microbiota, we cross-sectionally investigated the interplay between DAL, host metabolome, gut microbiota, and early-stage CKD (TwinsUK, n = 1,453). DAL was positively associated with CKD stage G1-G2 (Beta (95% confidence interval) = 0.34 (0.007; 0.7), p = 0.046). After adjusting for covariates and multiple testing, we identified 15 serum, 14 urine, 8 stool, and 7 saliva metabolites, primarily lipids and amino acids, associated with both DAL and CKD progression. Of these, 8 serum, 2 urine, and one stool metabolites were found to mediate the DAL-CKD association. Furthermore, the stool metabolite 5-methylhexanoate (i7:0) correlated with 26 gut microbial species. Our findings emphasize the gut microbiota's therapeutic potential in countering DAL's impact on CKD through the host metabolome. Interventional and longitudinal studies are needed to establish causality.

Placental efflux transporters and antiseizure or antidepressant medication use impact birth weight in MoBa cohort

Low birth weight raises neonatal risks and lifelong health issues and is linked to maternal medication use during pregnancy. We examined data from the Norwegian Mother, Father, and Child Cohort Study and the Medical Birth Registry of Norway, including 69,828 offspring with genotype data and 81,189 with maternal genotype data. We identified genetic risk variants in placental efflux transporters, calculated genetic scores based on alleles related to transporter activity, and assessed their interaction with prenatal use of antiseizure or antidepressant medication on offspring birth weight. Our study uncovered possible genetic variants in both offspring (rs3740066) and mothers (rs10248420; rs2235015) in placental efflux transporters (MRP2-ABCC2 and MDR1-ABCB1) that modulated the association between prenatal exposure to antiseizure medication and low birth weight in the offspring. Antidepressant exposure was associated with low birth weight, but there were no gene-drug interactions. The interplay between MRP2-ABCC2 and MDR1-ABCB1 variants and antiseizure medication may impact neonatal birth weight.

An integrative multi-omics approach to characterize interactions between tuberculosis and diabetes mellitus

Tuberculosis-diabetes mellitus (TB-DM) is linked to a distinct inflammatory profile, which can be assessed using multi-omics analyses. Here, a machine learning algorithm was applied to multi-platform data, including cytokines and gene expression in peripheral blood and eicosanoids in urine, in a Brazilian multi-center TB cohort. There were four clinical groups: TB-DM(n = 24), TB only(n = 28), DM(HbA1c ≥ 6.5%) only(n = 11), and a control group of close TB contacts who did not have TB or DM(n = 13). After cross-validation, baseline expression or abundance of MMP-28, LTE-4, 11-dTxB2, PGDM, FBXO6, SECTM1, and LINCO2009 differentiated the four patient groups. A distinct multi-omic-derived, dimensionally reduced, signature was associated with TB, regardless of glycemic status. SECTM1 and FBXO6 mRNA levels were positively correlated with sputum acid-fast bacilli grade in TB-DM. Values of the biomarkers decreased during the course of anti-TB therapy. Our study identified several markers associated with the pathophysiology of TB-DM that could be evaluated in future mechanistic investigations.

Bioinformatics analysis identifies TGF-β signaling pathway-associated molecular subtypes and gene signature in diabetic foot

The role of transforming growth factor β (TGF-β) in inflammation and immune response is established, but the mechanism of TGF-β signaling pathway-related genes (TRGs) in diabetic foot ulcer (DFU) is not fully understood. We aimed to investigate the contribution of TRGs in the identification, molecular categorization, and immune infiltration of DFU through bioinformatics analysis. TGF-β signaling pathway is activated in DFU. 33 TRGs were upregulated. Regression analysis revealed TGFBR1 and TGFB1 as significant differential expression core genes, validated by quantitative real-time PCR. The diagnostic model with core genes had high clinical validity (AUC = 0.909). Core gene expression was associated with immune cell infiltration. A total of 5672 genes showed differential expression in TGF-related patterns, with differences in biological functions and immune infiltration. TGF-β signaling pathway may be critical in DFU development.

Comparison of postoperative analgesia and side effects in pediatric laparoscopic surgery with morphine and nalbuphine

There is currently no consensus on the optimal perioperative pain management strategy involving specific opioids. This study aims to compare the postoperative analgesia, the associated side effects between nalbuphine and morphine in children undergoing laparoscopic surgery. One hundred ninety children were randomly assigned to nalbuphine (0.2 mg/kg) or morphine (0.2 mg/kg). Nalbuphine's analgesic effect was non-inferior to morphine, with similar total rescue analgesic consumption during PACU stay (0.03 ± 0.05mg vs. 0.04 ± 0.06 mg, p > 0.05). Nalbuphine group had a lower incidence of respiratory depression (RR ≤ 10/min) (4.8% vs. 38.6%, p < 0.001), PONV (2.4% vs. 18.1%, p = 0.002), and pruritus (0% vs. 16.9%, p < 0.001) than morphine. Additionally, nalbuphine showed a shorter laryngeal mask airway removal time (13.9 [12.7, 15.1]) compared with morphine (17.0 [15.1, 18.9], p = 0.011). Nalbuphine provides equipotent analgesia with significantly lower incidences of respiratory depression, PONV, and pruritus compared with morphine in pediatric laparoscopic surgery.

Independent prognostic value of CLDN6 in bladder cancer based on M2 macrophages related signature

M2 macrophages are associated with the prognosis of bladder cancer. CLDN6 has been linked to immune infiltration and is crucial for predicting the prognosis in multi-tumor. The effect of CLDN6 on M2 macrophages in bladder cancer remains elusive. Here, we compared a total of 40 machine learning algorithms, then selected optimal algorithm to develop M2 macrophages-related signature (MMRS) based on the identified M2 macrophages related module. MMRS predicted the prognosis better than other models and associated to immunotherapy response. CLDN6, as an important variable in MMRS, was an independent factor for poor prognosis. We found that CLDN6 was highly expressed and affected immune infiltration, immunotherapy response, and M2 macrophages polarization. Meanwhile, CLDN6 promoted the growth of bladder cancer and enhanced the carcinogenic effect by inducing polarization of M2 macrophages. In total, CLDN6 is an independent risk factor in MMRS to predict the prognosis of bladder cancer.

Risk of bias in exercise science: A systematic review of 340 studies

Risk of bias can contribute to irreproducible science and mislead decision making. Analyses of smaller subsections of the exercise science literature suggest many exercise science studies have unclear or high risk of bias. The current review (osf.io/jznv8) assesses whether this unclear or high risk of bias is more widespread in the exercise science literature and whether this bias has decreased since the publication of the 1996 Consolidated Standards of Reporting Trials (CONSORT) guidelines. We report significant reductions in selection, performance, detection, and reporting biases in 2020 compared with 1995 in the 340 of 5,451 studies assessed using the Cochrane Risk of Bias tool. Despite these improvements, most 2020 studies still had unclear or high risks of bias. These results underscore the need for methodological vigilance, adherence to reporting standards, and education on experimental bias. Factors contributing to these improvements, such advancements in education and journal requirements, remain uncertain.

Development of an obesity-related multi-gene prognostic model incorporating clinical characteristics in luminal breast cancer

Despite adjuvant chemotherapy and endocrine therapy in luminal breast cancer (LBC), relapses are common. Addressing this, we aim to develop a prognostic model to refine adjuvant therapy strategies, particularly for patients at high recurrence risk. Notably, obesity profoundly affects the tumor microenvironment (TME) of LBC. However, it is unclear whether obesity-related biological features can effectively screen high-risk patients. Utilizing weighted gene coexpression network analysis (WGCNA) on RNA sequencing (RNAseq) data, we identified seven obese LBC genes (OLGs) closely associated with patient prognosis. Subsequently, we developed a luminal obesity-gene clinical prognostic index (LOG-CPI), combining a 7-gene signature, TNM staging, and age. Its predictive efficacy was confirmed across validation datasets and a clinical cohort (5-year accuracy = 0.828, 0.760, 0.751, and 0.792, respectively). LOG-CPI emerges as a promising predictor for clinical prognosis and treatment response, helping distinguish molecular and immunological features in LBC patients and guiding clinical practice by identifying varying prognoses.

The role of cardiac remodeling associated with renal function in mediating cardiovascular event outcomes

The potential impact of renal function-related cardiovascular remodeling on associated cardiovascular risk has not been previously investigated. Hence, we conducted multiple mediation analyses in the UK Biobank study to evaluate this association. Using multiple Cox models, we found lower renal function (estimated glomerular filtration rate based on cystatin C, eGFR-cysC) was independently related to increased risks of various cardiovascular events and mortalities. Multivariable linear regression revealed a progressive relationship between declining eGFR-cysC and adverse left ventricular (LV) remodeling and impaired systolic function. In Cox models, larger LV volume, mass, as well as decreased systolic function, were significantly correlated with adverse events, particularly in heart failure. Mediation analyses showed that undesirable LV remodeling and cardiometabolic diseases were independent mediators. Our study explores the connections between reduced renal function and poor cardiovascular phenotypes, as well as their significant independent role in mediating renal function-cardiovascular outcome relationships.

SnoRNA profiling in colorectal cancer and assessment of non-invasive biomarker capacity by ddPCR in fecal samples

Small nucleolar RNAs (snoRNAs) have been identified dysregulated in several pathologies, and these alterations can be detected in tissues and in circulation. The main aim of this study was to analyze the whole snoRNome in advanced colorectal neoplasms and to identify new potential non-invasive snoRNA-based biomarkers in fecal samples by different analytical approaches. SNORA51, SNORD15B, SNORA54, SNORD12B, SNORD12C, SNORD72, SNORD89, and several members of SNORD115 and SNORD116 clusters were consistently deregulated in both tissue sets. After technical validation, SNORA51 and SNORD15B were detected in FIT+ samples. SNORA51 was significantly upregulated in FIT+ samples from CRC patients compared to healthy controls. This upregulation, together with the fecal hemoglobin concentration, was sufficient to identify, among FIT+ individuals, patients with CRC (AUC = 0.86) and individuals with advanced adenomas (AUC = 0.68). These findings portray snoRNAs as an alternative source of candidates for further studies and SNORA51 appears as a potential non-invasive biomarker for CRC detection.

Airway inflammation accelerates pulmonary exacerbations in cystic fibrosis

Airway inflammation underlies cystic fibrosis (CF) pulmonary exacerbations. In a prospective multicenter study of randomly selected, clinically stable adolescents and adults, we assessed relationships between 24 inflammation-associated molecules and the future occurrence of CF pulmonary exacerbation using proportional hazards models. We explored relationships for potential confounding or mediation by clinical factors and assessed sensitivities to treatments including CF transmembrane regulator (CFTR) protein synthesis modulators. Results from 114 participants, including seven on ivacaftor or lumacaftor-ivacaftor, representative of the US CF population during the study period, identified 10 biomarkers associated with future exacerbations mediated by percent predicted forced expiratory volume in 1 s. The findings were not sensitive to anti-inflammatory, antibiotic, and CFTR modulator treatments. The analyses suggest that combination treatments addressing RAGE-axis inflammation, protease-mediated injury, and oxidative stress might prevent pulmonary exacerbations. Our work may apply to other airway inflammatory diseases such as bronchiectasis and the acute respiratory distress syndrome.

Social influence, performance expectancy, and price value as determinants of telemedicine services acceptance in Chile

Medicine is a discipline based on and nurtured by scientific research and technological development. The use of health services supported by information technology is increasing worldwide, and Latin America is no exception. Factors such as needing more specialists in peripheral cities, large geographic areas, and socio-cultural aspects limit the possibility of receiving timely and quality medical care services. Information Technology (IT) for health purposes, such as e-health, is a cost-effective solution for equitable access to quality healthcare services and optimization of the rising associated costs. As an e-health service, telemedicine facilitates and mediates distance communication between the patient and medical staff. Even though Latin America is at the beginning of the development of telemedicine, it would have a relevant impact, given the geographic and socioeconomic conditions of the population in this part of the world. Drawing on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) theory, we developed a theoretical model to identify the latent factors influencing the public acceptance of telemedicine and examined their interrelationships. A survey questionnaire was designed and administered to 391 residents in Antofagasta, a mine region of Chile. After that, structural equation modeling was employed to analyze the survey data. The results reveal that the UTAUT2 factors' performance expectancy, social influence, and price value significantly impact the intention to use (R 2 = 0.693). Additionally, the model presented a good fit. This study enriches the existing theoretical research on the acceptance of telemedicine services and offers insights into understanding and managing technology in the Chilean health sector.

Thiazolidinediones Are Partially Effective Bitter Blockers

PURPOSE

The bad bitter taste of some medicines is a barrier to overcoming noncompliance with medication use, especially life-saving drugs given to children and the elderly. Here, we evaluated a new class of bitter blockers (thiazolidinediones, TZDs).

METHODS

In this study, 2 TZDs were tested, rosiglitazone (ROSI) and a simpler form of TZD, using a high-potency sweetener as a positive control (neohesperidin dihydrochalcone, NHDC). We tested bitter-blocking effects using the bitter drugs tenofovir alafenamide fumarate (TAF), a treatment for HIV and hepatitis B infection, and praziquantel (PRAZ), a treatment for schistosomiasis, by conducting taste testing with 2 separate taste panels: a general panel (N = 97, 20-23 years, 82.5% female, all Eastern European) and a genetically informative panel (N = 158, including 68 twin pairs, 18-82 years, 76% female, 87% European ancestry). Participants rated the bitterness intensity of the solutions on a 100-point generalized visual analog scale.

FINDINGS

Person-to-person differences in drug bitterness were striking; TAF and PRAZ were weakly or not bitter for some people but moderately to highly bitter for others. Participants in both taste panels rated the bitter drugs TAF and PRAZ as less bitter on average when mixed with NHDC than when sampled alone. ROSI partially suppressed the bitterness of TAF and PRAZ, but effectiveness differed between the 2 panels: bitterness was significantly reduced for PRAZ but not TAF in the general panel and for TAF but not PRAZ in the genetically informative panel. ROSI was a more effective blocker than the other TZD.

IMPLICATIONS

These results suggest that TZDs are partially effective bitter blockers and the suppression efficacy differs from drug to drug, from person to person, and from panel to panel, suggesting other TZDs should be designed and tested with more drugs and on diverse populations to define which ones work best with which drugs and for whom. The discovery of bitter receptor blockers can improve compliance with medication use.

Estimating the cost of sports injuries: A scoping review

OBJECTIVES

Provide an overview of the methods used to estimate the cost of sports-related injury published to date, and to highlight considerations and opportunities for future research.

DESIGN

Scoping review.

METHODS

Scopus, MEDLINE and CINHAL were searched from 1st January 2000 to 1st January 2023. Studies were screened by two independent reviewers and were eligible if they reported on a cost analysis or cost estimation of sports related injury.

RESULTS

Thirty-one studies fulfilled the inclusion criteria. Twenty-seven studies (87 %) were published since 2014. The type of costs included direct healthcare costs (12 studies), indirect costs (10 studies) and a combination of both (9 studies). Twenty-one studies (68 %) used a bottom-up costing approach to measure costs of sports injury and estimated direct costs from the service rates or fee schedules of health systems, hospital, insurance companies or national insurance boards. A top-down approach was used in seven studies (23 %) to estimate the indirect salary cost of time-loss injuries using data from publicly available resources. Ten studies were from the cost perspective of a sporting organisation (32 %). There was a lack of explicit reporting of the costing method used and the perspective of those bearing the costs.

CONCLUSIONS

Estimating the cost of sports injuries is an emerging area of research, with publications increasing in recent years. However, there remains a lack of methodological guidance to inform or appraise these studies. The expansion of established cost of illness checklists with sport injury explanations to guide future cost of sports injury studies is recommended.

[Construct validity of the TECS questionnaire for the quality of teamwork in surgery]

Objective

To assess the construct validity and reliability of the Trabajo en Equipo en Cirugía (TECS) questionnaire.

Method

The questionnaire was administered to 401 undergraduate students who were doing surgery practices at three universities. An exploratory factor analysis was performed on the first 200 observations, and a confirmatory factor analysis on the remaining ones. The reliability of the instrument was established with Cronbach's alpha.

Results

The average age of the study population was 22 years (± 1.4) and 65.5% were women. The factors "Student's disposition towards teamwork", "Structure of the work environment" and "Leadership and collaboration in the work team" showed excellent internal consistency with a Cronbach's alpha of 0.94. The results in the exploratory factor analysis showed adequate goodness of fit with the empirical data.

Conclusions

The TECS is a valid (content and construct) and reliable instrument to assess the quality of teamwork in surgery in medical students.

Analyzing the application of mixed method methodology in medical education: a qualitative study

BACKGROUND

Interest in mixed methods methodology within medical education research has seen a notable increase in the past two decades, yet its utilization remains less prominent compared to quantitative methods. This study aimed to investigate the application and integration of mixed methods methodology in medical education research, with a specific focus on researchers' perceptions, strategies, and readiness, including the necessary skills and expertise. This study adheres to the COREQ guidelines for reporting qualitative research.

METHODS

Faculty members from King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Saudi Arabia, across its three campuses in Riyadh, Jeddah, and Al Ahsa, participated in this study during the 2021-2022 academic year. We conducted 15 in-depth, one-on-one interviews with researchers who had previously used mixed methods in their medical education research. Theoretical saturation was reached with no refusals or dropouts. Data were collected using a semi-structured interview guide developed from literature review and mixed methods guidelines. Thematic analysis was employed to analyze the data, enabling a comprehensive understanding of the participants' perspectives.

RESULTS

The thematic analysis of the interviews yielded three key themes. The first theme, 'Understanding and Perceptions of Mixed Methods in Medical Education Research,' delved into researchers' depth of knowledge and conceptualization of mixed methods. The second theme, 'Strategies and Integration in Mixed Methods Implementation,' explored how these methodologies are applied and the challenges involved in their integration. The final theme, 'Mastery in Mixed Methods: Prerequisites and Expert Consultation in Research,' highlighted the gaps in readiness and expertise among researchers, emphasizing the importance of expert guidance in this field.

CONCLUSION

Findings indicate a varied understanding of mixed methods among participants. Some lacked a comprehensive grasp of its application, while others perceived mixed methods primarily as a means to enhance the publication prospects of their studies. There was a general lack of recognition of mixed methods as a guiding methodology for all study aspects, pointing to the need for more in-depth training and resources in this area.

How ChatGPT works: a mini review

OBJECTIVE

This paper offers a mini-review of OpenAI's language model, ChatGPT, detailing its mechanisms, applications in healthcare, and comparisons with other large language models (LLMs).

METHODS

The underlying technology of ChatGPT is outlined, focusing on its neural network architecture, training process, and the role of key elements such as input embedding, encoder, decoder, attention mechanism, and output projection. The advancements in GPT-4, including its capacity for internet connection and the integration of plugins for enhanced functionality are discussed.

RESULTS

ChatGPT can generate creative, coherent, and contextually relevant sentences, making it a valuable tool in healthcare for patient engagement, medical education, and clinical decision support. Yet, like other LLMs, it has limitations, including a lack of common sense knowledge, a propensity for hallucination of facts, a restricted context window, and potential privacy concerns.

CONCLUSION

Despite the limitations, LLMs like ChatGPT offer transformative possibilities for healthcare. With ongoing research in model interpretability, common-sense reasoning, and handling of longer context windows, their potential is vast. It is crucial for healthcare professionals to remain informed about these technologies and consider their ethical integration into practice.

Efficacy and safety of rifampicin-based triple therapy for non-puerperal mastitis: A single-arm, open-label, prospective clinical trial

OBJECTIVES

To assess the efficacy and safety of rifampicin-based triple therapy (rifampicin, isoniazid, and ethambutol) for treating NPM.

METHODS

This single-center, single-arm, prospective clinical trial was conducted at the Second Hospital of Shandong University (Jinan, China). Patients with pathologically diagnosed granulomatous lobular mastitis and periductal mastitis received triple drugs, i.e., rifampicin (450 mg/day), isoniazid (300 mg/day), and ethambutol (15 mg/kg/day), until complete response or the investigator decided to discontinue treatment. The primary endpoint was the complete response rate (CRR) assessed by the investigator. The secondary endpoints included the overall remission rate (ORR), recurrence rate (RR), and safety.

RESULTS

A total of 218 patients were enrolled in the study between January 1, 2013 and October 31, 2020. With a median follow-up time of 48 months, the CRR and the ORR were 78.44% and 94.04%, respectively. While 13 patients (5.96%) demonstrated no response and 19 relapsed (8.72%). Adverse events (AEs) were not common. The most common AEs during treatment were liver dysfunction (1.83%), gastrointestinal reactions (1.83%), fatigue (1.83%), erythema (1.38%), and menstrual disorders (0.92%).

CONCLUSION

Rifampicin, isoniazid, and ethambutol demonstrated promising response rates with acceptable safety profiles in patients with NPM. Further confirmatory trial is warranted in the future.

TRIAL REGISTRATION

The study was approved by the Ethics Committee of the Second Hospital of Shandong University and retrospectively registered at the China Clinical Trial Registration Center (registration number: ChiCTR2100049591).

[Virtual reality in teaching using mentally ill patient avatars]

BACKGROUND

Medical interaction and exploration techniques are the most important tools that medical students have to acquire in the subject of psychiatry and psychotherapy. The new digital technologies currently available, such as virtual reality (VR), as important supplements can contribute to a significant improvement in the teaching of psychiatric-psychopathological learning content as well as, in particular, the technique of ascertaining the psychiatric history and diagnosis.

OBJECTIVE

Evaluation of the Bochum Avatar Exploration Project (AVEX) as part of the curricular course in medical studies at the Ruhr University Bochum for its possibilities to convey learning content and techniques of anamnesis and diagnosis in the subject of psychiatry and psychotherapy.

METHODS

In AVEX, a total of 87 medical students in the clinical study section have so far been able to enter into a dialogue with "mentally ill" avatars and gain experience with VR technology as a learning and teaching method in the subject of psychiatry and psychotherapy.

RESULTS

Despite the limited possibilities for interaction with the digital avatars, it is possible to achieve a substantial transfer of learning content in psychiatry; however, the students must be well supported by the lecturers.

CONCLUSION

The AVEX project already shows promising possibilities for supplementing the teaching of medical students, even if the fit of questions and replies in dialogue with the virtual avatars still needs to be improved. As advances in the linguistic communication of emotions and the visual effects of the avatar representation can be predicted, the significance of this technology will continue to increase.

Endorsed midwives prescribing scheduled medicines in Australia: A scoping review

PROBLEM

Despite 10 years of prescribing scheduled medicines by Endorsed Midwives, little is known about prescribing practices.

BACKGROUND

Endorsed Midwives can prescribe scheduled medicines and have access to Medicare rebates to support service provision. Endorsed Midwives have the potential to improve access to medications for women, however, are met with barriers, including inconsistencies in state and national legislation.

AIM

To search for what is published regarding Endorsed Midwife prescribing of scheduled medicines in Australia, report on the literature, synthesise the findings and discuss the results.

METHODS

A scoping review utilising the Joanna Brigg's Institute methodology. A search of CINAHL, PubMed, Science Direct and Medline databases was conducted. Seven peer-reviewed articles were identified; three discussion papers, one literature review and three research papers, published between 2016 and 2023 in English. Qualitative content analysis was used to identify topic areas.

FINDINGS

Four topic areas were identified: 1) Endorsed Midwives increase women's access to prescribed medications; 2) the Pharmaceutical Benefits Scheme is restrictive and diminishes midwifery prescribing; 3) medication prescribing depends on internal and external structures; 4) professional relationships support prescribing.

DISCUSSION

The authority to prescribe augments Endorsed Midwives' practice, improves timely access to medications and enhances role satisfaction. The effective use of midwifery prescribing is hampered by barriers such as the Pharmaceutical Benefits Scheme, inappropriate medication formularies, and poorly designed health service policy.

CONCLUSION

To fully utilise Endorsed Midwife prescribing in all settings of maternity care, further work is required to develop education, remove barriers, and demonstrate the safety and effectiveness of midwifery prescribing.

Women in Radiology: A perspective from Spain

INTRODUCTION

There are gender inequalities in all fields, including radiology. Although the situation is improving, the presence of radiologists in leadership positions continues to be a minority. The objective of this article is to analyse the situation of women in the spanish radiology, comparing it with Europe and the United States.

MATERIALS AND METHODS

We selected the years 2000-2022 as reference period to make a comparison with feminization data throughout history. In addition, relevant specific data from the just begun 2023 were also included. The variables in which we investigated feminization were the following: medical students, medical graduates, radiology residents and specialists, section chiefs, department chairs, radiology residency programme directors, radiology university professors, presidents of the main radiological entities and societies in Spain, Europe and the United States, recipients of the main awards given by these radiological societies and chief editors of their journals. In order to perform this analysis we conducted an in-depth bibliographic research, we contacted the radiological societies of Spain, Europe and the USA and we carried out a survey in the main Spanish radiology departments.

RESULTS

The female presence in radiology decreases as we rise to leadership positions, a situation that is patent in Spain, Europe and the US, comparison that will be analysed in depth throughout the article. In Spanish hospitals in 2021 there were 58.1% female radiology residents, 55% female radiologists, 42.9% female section chiefs and 24.4% female department chairs. In SERAM's history there have been 10% female presidents, 22% female gold medallists and 5% female editors-in-chief. If we analyse data from 2000 to 2023, female presidents reach 32% and female gold medallists 31%.

CONCLUSIONS

Although gender inequality is declining, in radiology women continue to be underrepresented in leadership positions. Work must be done in order to build a diverse and inclusive profession that reflects demographic reality.

An obscured obstacle-Prevalence and nature of micro aggressions amongst healthcare professionals

INTRODUCTION

Microaggressions create negative consequences on the mental health of individuals who experience them, such as feelings of alienation, frustration and low self-esteem. Physicians worldwide are negatively impacted by the detrimental effects of microaggressions and implicit bias. It is imperative to establish the prevalence specificity of the problem hence the aim of this study is to determine the prevalence, nature and determinants of microaggressions amongst healthcare professionals.

METHOD

The study used an online anonymous survey to collect data including demographics, awareness of the term, experience of microaggression, acts and response. The research findings were analyzed using univariate and multivariate analyses using Chi-square test and binary logistic regression respectively.

RESULT

A total of 443 participants (40.9% males, 59.1% females) included 403 physicians (91%), 21 dentists (4.7%), 15 nurses (3.4%) and 4 pharmacists (0.9%). More than half of the participants (59.8%) were aware of the term micro-aggression. The percentage was significantly higher among respondents from the western region of Saudi Arabia than the Gulf/Middle Eastern countries. Approximately 38.1% of the participants experienced microaggression and more than half (55.62%) did not report experiencing microaggression. The most common form of microaggression was passive-aggressive behavior (80.5%) followed by invalidation of an opinion (73.4%). Among those who experienced microaggression, (12.9%) reported anger as the most predominant emotional response.

CONCLUSION

Microaggression is a universal phenomenon. Further research is necessary to determine its prevalence in other countries to establish a comprehensive understanding of its cultural context.

Evaluation of ChatGPT-generated medical responses: A systematic review and meta-analysis

OBJECTIVE

Large language models (LLMs) such as ChatGPT are increasingly explored in medical domains. However, the absence of standard guidelines for performance evaluation has led to methodological inconsistencies. This study aims to summarize the available evidence on evaluating ChatGPT's performance in answering medical questions and provide direction for future research.

METHODS

An extensive literature search was conducted on June 15, 2023, across ten medical databases. The keyword used was "ChatGPT," without restrictions on publication type, language, or date. Studies evaluating ChatGPT's performance in answering medical questions were included. Exclusions comprised review articles, comments, patents, non-medical evaluations of ChatGPT, and preprint studies. Data was extracted on general study characteristics, question sources, conversation processes, assessment metrics, and performance of ChatGPT. An evaluation framework for LLM in medical inquiries was proposed by integrating insights from selected literature. This study is registered with PROSPERO, CRD42023456327.

RESULTS

A total of 3520 articles were identified, of which 60 were reviewed and summarized in this paper and 17 were included in the meta-analysis. ChatGPT displayed an overall integrated accuracy of 56 % (95 % CI: 51 %-60 %, I2 = 87 %) in addressing medical queries. However, the studies varied in question resource, question-asking process, and evaluation metrics. As per our proposed evaluation framework, many studies failed to report methodological details, such as the date of inquiry, version of ChatGPT, and inter-rater consistency.

CONCLUSION

This review reveals ChatGPT's potential in addressing medical inquiries, but the heterogeneity of the study design and insufficient reporting might affect the results' reliability. Our proposed evaluation framework provides insights for the future study design and transparent reporting of LLM in responding to medical questions.

Physically active primary care physicians consult more on physical activity and exercise for patients: A public teaching-hospital study

Physical activity and exercise (PAE) improve quality of life and reduce the effects of chronic diseases. Primary care physicians (PCPs) play an important role to encourage PAE in patients. We aim to assess PCPs' current PAE consultation practices and their enablers/barriers in daily clinical practice. We had 64 PCPs (age [35.3 ​± ​4.7] y, 47 women) that completed self-administered questionnaires on PAE consultation practices, training, and confidence levels. PCPs (n ​= ​42) also completed the International Physical Activity Questionnaire-Short Form to assess their physical activity (PA) levels. We conducted correlation, one-way analysis of variance and a linear regression to assess the associations between enablers, barriers and PA levels to PAE consultation practices. On average, PCPs consulted on PAE in 49.7% of their daily clinical appointments. Majority of PCPs (70%) strongly agreed that more PAE knowledge were needed to increase consultation practices. Top three barriers related (p ​< ​0.001) to practices were lack of PAE education (r ​= ​0.47), patients' preference of pharmaceutical interventions (r ​= ​0.45) and lack of continuing education in PAE for PCPs (r ​= ​0.37). Physically active PCPs (health-enhancing PA levels, n ​= ​6) gave significantly more daily consultations in PAE, 73.2% ​± ​21.9%, compared to inactive PCPs (n ​= ​13), 37.4% ​± ​22.8% (p ​= ​0.013). In our regression output, PCPs who had higher PA levels consulted more on PAE daily (R2 ​= ​0.38, p ​< ​0.001) while controlling for age. Conclusion, PCPs require more knowledge on PAE and need be physically active themselves to increase PAE consultation for patients in their daily practice. Medical education should consider including more PA and exercise topics that may benefit both physicians and their patients.

Life cycle assessment of pharmaceutical packaging addressing end-of-life alternatives

Pharmaceutical packaging waste has increased due to a higher global demand for pharmaceutical products, leading to more waste generation and associated environmental impacts. The main goal of this article is to present a cradle-to-grave life cycle assessment of pharmaceutical packaging, evaluating end-of-life (EoL) alternatives, aiming to identify hotspots and opportunities for improvement. A life cycle model was implemented for three types of pharmaceutical packaging (blisters, sachets, bottles; 23 packaging). The functional unit is the storage and delivery of medicines containing the same active pharmaceutical ingredient, dosage, and amount of medicines. Two EoL analyses were performed: 1) compare take-back (recycling and incineration) with domestic waste collection (landfill or incineration); and 2) assess different EoL situations of pharmaceutical packaging in Europe. A life cycle impact assessment was performed for 13 categories. Analysis 1 shows that take-back presents lower environmental impacts than domestic waste collection for seven out of 13 categories due to paper and glass recycling benefits. Analysis 2 shows that in the take-back, higher amounts of packaging are recycled or incinerated, which leads to lower EoL impacts. A sensitivity analysis was performed to evaluate the influence of parameters and assumptions in packaging EoL impacts. Packaging production contributes significantly to life cycle impacts, followed by transportation, EoL, and packing process. Ecodesign initiatives are recommended, such as packaging with less material and volume, using materials with lower impacts to significantly reduce the impacts of pharmaceutical packaging.

Plant-Derived Natural Products for the Treatment of Bacterial Infections

Bacterial infections are a significant public health concern, and the emergence of antibiotic-resistant bacteria (ARB) has become a major challenge for modern medicine. The overuse and misuse of antibiotics have contributed to the development of ARB, which has led to the need for alternative therapies. Plant-derived natural products (PNPs) have been extensively studied for their potential as alternative therapies for the treatment of bacterial infections. The diverse chemical compounds found in plants have shown significant antibacterial properties, making them a promising source of novel antibacterial agents. The use of PNPs as antibacterial agents is particularly appealing because they offer a relatively safe and cost-effective approach to the treatment of bacterial infections. This chapter aims to provide an overview of the current state of research on PNPs as antibacterial agents. It will cover the mechanisms of action of the main PNPs against bacterial pathogens and discuss their potential to be used as complementary therapies to combat ARB. This chapter will also highlight the most common screening methodologies to discover new PNPs and the challenges and future prospects in the development of these compounds as antibacterial agents.

Engineering innovations in medicine and biology: Revolutionizing patient care through mechanical solutions

The overlap between mechanical engineering and medicine is expanding more and more over the years. Engineers are now using their expertise to design and create functional biomaterials and are continually collaborating with physicians to improve patient health. In this review, we explore the state of scientific knowledge in the areas of biomaterials, biomechanics, nanomechanics, and computational fluid dynamics (CFD) in relation to the pharmaceutical and medical industry. Focusing on current research and breakthroughs, we provide an overview of how these fields are being used to create new technologies for medical treatments of human patients. Barriers and constraints in these fields, as well as ways to overcome them, are also described in this review. Finally, the potential for future advances in biomaterials to fundamentally change the current approach to medicine and biology is also discussed.

Complementary feature learning across multiple heterogeneous networks and multimodal attribute learning for predicting disease-related miRNAs

Inferring the latent disease-related miRNAs is helpful for providing a deep insight into observing the disease pathogenesis. We propose a method, CMMDA, to encode and integrate the context relationship among multiple heterogeneous networks, the complementary information across these networks, and the pairwise multimodal attributes. We first established multiple heterogeneous networks according to the diverse disease similarities. The feature representation embedding the context relationship is formulated for each miRNA (disease) node based on transformer. We designed a co-attention fusion mechanism to encode the complementary information among multiple networks. In terms of a pair of miRNA and disease nodes, the pairwise attributes from multiple networks form a multimodal attribute embedding. A module based on depthwise separable convolution is constructed to enhance the encoding of the specific features from each modality. The experimental results and the ablation studies show that CMMDA's superior performance and the effectiveness of its major innovations.

The importance of proinflammatory failed-repair tubular epithelia as a predictor of diabetic kidney disease progression

The immense public health burden of diabetic kidney disease (DKD) has led to an increase in research on the pathophysiology of advanced DKD. The present study focused on the significance of proinflammatory vascular cell adhesion molecule 1 (VCAM1)+ tubules in DKD progression. A retrospective cohort study of DKD patients showed that the percentage of VCAM1+ tubules in kidney samples was correlated with poor renal outcomes. We established an advanced DKD model by partial resection of the kidneys of db/db mice and demonstrated that it closely resembled the human advanced DKD phenotype, with tissue hypoxia, tubular DNA damage, tissue inflammation, and high tubular VCAM1 expression. Luseogliflozin ameliorated tissue hypoxia and proinflammatory responses, including VCAM1+ expression, in tubules. These findings suggest the potential of tubular VCAM1 as a histological marker for poor DKD outcomes. SGLT2 inhibitors may attenuate tissue hypoxia and subsequent tissue inflammation in advanced DKD, thereby ameliorating tubular injury.

Evaluation of GPT-4 for 10-year cardiovascular risk prediction: Insights from the UK Biobank and KoGES data

Cardiovascular disease (CVD) remains a pressing global health concern. While traditional risk prediction methods such as the Framingham and American College of Cardiology/American Heart Association (ACC/AHA) risk scores have been widely used in the practice, artificial intelligence (AI), especially GPT-4, offers new opportunities. Utilizing large scale of multi-center data from 47,468 UK Biobank participants and 5,718 KoGES participants, this study quantitatively evaluated the predictive capabilities of GPT-4 in comparison with traditional models. Our results suggest that the GPT-based score showed commendably comparable performance in CVD prediction when compared to traditional models (AUROC on UKB: 0.725 for GPT-4, 0.733 for ACC/AHA, 0.728 for Framingham; KoGES: 0.664 for GPT-4, 0.674 for ACC/AHA, 0.675 for Framingham). Even with omission of certain variables, GPT-4's performance was robust, demonstrating its adaptability to data-scarce situations. In conclusion, this study emphasizes the promising role of GPT-4 in predicting CVD risks across varied ethnic datasets, pointing toward its expansive future applications in the medical practice.

Moderate intensity exercise may protect cardiac function by influencing spleen microbiome composition

The beneficial effects of physical exercise on human cardiorespiratory fitness might be through reduced systemic inflammation, but the mechanism remains a controversy. Recent studies have highlighted the importance of spleen microbiomes in immune regulation. Hence, we conducted a study using a high-fat diet and exercise mouse model to investigate the relationships among different exercise intensities, spleen microbiome composition, and cardiac function. The mice spleen contained a diverse array of microbiota. Different intensities of exercise resulted in varying compositions of the spleen microbiome, Treg cell levels, and mouse heart function. Additionally, the abundance of Lactobacillus johnsonii in the mouse spleen exhibited a positive correlation with Treg cell levels, suggesting that Lactobacillus johnsonii may contribute to the production of Treg cells, potentially explaining the protective role of moderate-intensity exercise on cardiac function. In conclusion, our findings provide evidence that moderate-intensity exercise may promote cardiac function protection by influencing the spleen microbiome composition.