Medical Research Articles

Ex vivo electrical bioimpedance measurements and Cole modelling on the porcine colon and rectum

Different pathological changes in the large intestine wall, associated with the development of different chronic diseases, including colorectal cancer, could be reflected in electrical bioimpedance readings. Thickness and composition of the mucus bilayer covering it in the luminal side, abundance of bacteria of the intestinal microbiota, the permeability of the epithelium and inflammation are some of these. However, scientific literature on electrical passive properties of the large intestine is scarce. In this study, complex impedance measurements at 8 frequencies were carried out on 6 specimens of porcine colorectal tissue, within half ab hour post-mortem, obtained from a local abattoir. For 5 different distances, measured proximally from the border of the anus, 3 readings were taken at 3 different points with a tetrapolar probe. The results show 2 different dielectric dispersions in the α and β regions and it seems that there is a relationship between the values of resistivities and the thickness of the wall. Also, parameter values both for the Cole and the geometrical models are given. Another set of electrical bioimpedance readings was carried out in order to assess the effect of the mucus layer on electrical properties of the tissue. It seems that these layers are related to the low frequency dispersion. Finally, electrical passive properties of porcine colorectal tissue, reported in this work, give reference values and behaviour patterns that could be applied for further research in human medicine, based on bioimpedance measurements.

In-Situ and Green Synthesis of Silk Fibroin-Silver Nanoparticles Composite Microfibers for Enhanced Antibacterial Applications.

The antibacterial properties of modified silk fibroin microfibers (SF MFs) have been widely studied. Among various modifications, integration of silver nanoparticles (Ag NPs) and SF MFs has garnered significant attention due to the broad-spectrum antibacterial activities and long-term antibacterial effect of Ag nanomaterials. However, the traditional introduction of reducing agents or other additives during the synthesis of Ag-SF composite MFs potentially affects their structure and antibacterial properties. Facile, green and effective methods for the preparation of Ag-SF MFs with enhanced antibacterial properties are therefore highly desired. In this study, Ag NPs were uniformly in-situ deposited onto the optimized SF MFs by adjusting the pH and duration conditions under the guidance of green chemistry. The loaded Ag NPs have a good dispersibility and an average size of ~10 nm. The stability of SF MFs after the deposition of Ag NPs and the crystalline features of the loaded Ag NPs have been carefully investigated. Moreover, antibacterial experiments confirmed that Ag-SF MFs exhibited superior antibacterial activities. After co-incubating Ag-SF MFs with L929 cells, the cell viability reached 90%, demonstrating the great biocompatibility of the modified fibers. This green in-situ synthetic method will promote the further medical use of Ag-SF MFs in antibacterial fields.

Assessing the quality and readability of online patient information: ENT UK patient information e-leaflets vs responses by a Generative Artificial Intelligence.

The evolution of artificial intelligence has introduced new ways to disseminate health information, including natural language processing models like ChatGPT. However, the quality and readability of such digitally-generated information remains understudied. This study is the first to compare the quality and readability of digitally-generated health information against leaflets produced by professionals. Patient information leaflets for five ENT UK leaflets and their corresponding ChatGPT responses were extracted from the Internet. Assessors with various degree of medical knowledge evaluated the content using the Ensuring Quality Information for Patients (EQIP) tool and readability tools including the Flesch-Kincaid Grade Level (FKGL). Statistical analysis was performed to identify differences between leaflets, assessors, and sources of information. ENT UK leaflets were of moderate quality, scoring a median EQIP of 23. Statistically significant differences in overall EQIP score were identified between ENT UK leaflets but ChatGPT responses were of uniform quality. Non-specialist doctors rated the highest EQIP scores while medical students scored the lowest. The mean readability of ENT UK leaflets was higher than ChatGPT responses. The information metrics of ENT UK leaflets were moderate and varied between topics. Equivalent ChatGPT information provided comparable content quality, but with reduced readability. ChatGPT patient information and professionally-produced leaflets had comparable content, but LLM content were required a higher reading age. With the increasing use of online health resources, this study highlights the need for a balanced approach that considers optimises both the quality and readability of patient education materials.

Towards explainable oral cancer recognition: Screening on imperfect images via Informed Deep Learning and Case-Based Reasoning

Oral squamous cell carcinoma recognition presents a challenge due to late diagnosis and costly data acquisition. A cost-efficient, computerized screening system is crucial for early disease detection, minimizing the need for expert intervention and expensive analysis. Besides, transparency is essential to align these systems with critical sector applications. Explainable Artificial Intelligence (XAI) provides techniques for understanding models. However, current XAI is mostly data-driven and focused on addressing developers’ requirements of improving models rather than clinical users’ demands for expressing relevant insights. Among different XAI strategies, we propose a solution composed of Case-Based Reasoning paradigm to provide visual output explanations and Informed Deep Learning (IDL) to integrate medical knowledge within the system. A key aspect of our solution lies in its capability to handle data imperfections, including labeling inaccuracies and artifacts, thanks to an ensemble architecture on top of the deep learning (DL) workflow. We conducted several experimental benchmarks on a dataset collected in collaboration with medical centers. Our findings reveal that employing the IDL approach yields an accuracy of 85%, surpassing the 77% accuracy achieved by DL alone. Furthermore, we measured the human-centered explainability of the two approaches and IDL generates explanations more congruent with the clinical user demands.

Allergens causing contact dermatitis of the feet: Investigation and analysis of allergic reaction causes.

Allergic contact dermatitis of the feet is a significant problem that affects the quality of life and requires attention from the medical community due to the number of studied and still unidentified allergens. The purpose of this review article is to summarize the available scientific data regarding the most common allergens that cause ACD of the feet. Nickel sulphate, neomycin sulphate, thiuram mix and colophony occupy a significant place in the prevalence of allergies. The prevalence of sensitization to rubber and leather products can vary depending on the ethno-demographic characteristics of the country, as well as the specifics of a person's professional activity, such as the use of protective waterproof shoes, increased humidity of the microclimate, and atopy in anamnesis. Patch testing has been shown to be an important method for identifying allergens, however, not all footwear components are tested during patch testing with standard allergen series, requiring the use of patches made from patient shoe samples. Expanding the scope of patch testing to include other possible allergens is important for the accurate diagnosis of ACD of the feet and a more detailed study of those allergens that were previously considered rare.

Development of a core outcome set of clinical research on the integration of traditional Chinese and Western medicine for spinal metastases: a study protocol

BackgroundIn recent years, the incidence of spinal metastasis (SM) has been increasing steadily. In response to this serious public health problem, researchers have made progress by using the integration of...

Combination of Youhua Kuijie Prescription and sulfasalazine can alleviate experimental colitis via IL-6/JAK2/STAT3 pathway

IntroductionYouhua Kuijie prescription (YHKJ) is a hospital preparation that is composed of nine kinds of herbs. Sulfasalazine (SASP) is widely used as a first-line clinical treatment for UC. Traditional Chinese medicine and Western medicine have their own advantages in the treatment of UC, and the mechanism of YHKJ combined with SASP in the treatment of UC needs to be investigated.MethodsIn this study, the therapeutic mechanism of YHKJ combined with SASP in the treatment of UC was predicted by network pharmacology and molecular docking. The chemical components and related targets of YHKJ were obtained from the TCMSP database. The chemical structure of SASP was obtained from the PubChem server, and related targets of SASP molecules were identified using the PharmMapper database. UC-related targets were obtained from the DisGeNET, GeneCards, OMIM, TTD, DrugBank and PharmGkb databases.ResultsIn total, 197 shared targets were identified by constructing a Venn diagram. PPI network data obtained from the STRING database were imported into Cytoscape to visualize the “drug-disease” target network, and STAT3 was selected as the core target by topological analysis. Gene Ontology revealed the biological functions of target genes, and KEGG analysis revealed that the core target STAT3 was differentially expressed in Th17 cells and the JAK-STAT signaling pathway. Thus, the core target STAT3 was subjected to molecular docking with the top 10 components, including nine YHKJ components (quercetin, luteolin, ursolic acid, daidzein, kaempferol, wogonin, myricetin, formononetin, indirubin) and SASP (C18H14N4O5S). The molecular docking results showed that STAT3 had favorable binding with the nine YHKJ components and SASP; STAT3 had the strongest binding with ursolic acid (−10.26 kcal/mol), followed by SASP (−8.54 kcal/mol). Qualitative analysis of the chemical constituents of YHKJ by HPLC revealed that sitosterol, ursolic acid, myricetin, daidzein, quercetin, kaempferol and formononetin were the main components. Additional experiments verified that YHKJ combined with SASP inhibited activation of the IL-6/JAK2/STAT3 pathway and alleviated inflammation in UC model rats.DiscussionOur results showed that seven chemical components in YHKJ cooperate with SASP to interfere with activation of the IL-6/JAK2/STAT3 pathway, thus playing a role in the treatment of UC.

Regulating algorithmic care in the European Union: evolving doctor-patient models through the Artificial Intelligence Act (AI-Act) and the liability directives.

This article argues that the integration of artificial intelligence (AI) into healthcare, particularly under the European Union's Artificial Intelligence Act (AI-Act), poses significant implications for the doctor-patient relationship. While historically paternalistic, Western medicine now emphasises patient autonomy within a consumeristic paradigm, aided by technological advancements. However, hospitals worldwide are adopting AI more rapidly than before, potentially reshaping patient care dynamics. Three potential pathways emerge: enhanced patient autonomy, increased doctor control via AI, or disempowerment of both parties as decision-making shifts to private entities. This article contends that without addressing flaws in the AI-Act's risk-based approach, private entities could be empowered at the expense of patient autonomy. While proposed directives like the AI Liability Directive (AILD) and the revised Directive on Liability for Defective Products (revised PLD) aim to mitigate risks, they may not address the limitations of the AI-Act. Caution must be exercised in the future interpretation of the emerging regulatory architecture to protect patient autonomy and to preserve the central role of healthcare professionals in the care of their patients.

Free Text Analysis: Identification of adverse drug events in clinical notes

ObjectiveTo explore the prevalence of adverse drug events (ADE) leading to drug changes using Artificial Intelligence (AI) based free text analysis of primary care encounter notes . ApproachWe used electronic medical record l encounter notes linked to population-based drug dispensation records. Dispensation records were used to identify drug switching to another agent in the same therapeutic class, which could suggest an undesirable side-effect requiring discontinuation of the drug. We annotated clinical notes by identifying ADEs after retrieving the last encounter note written for that patient on or before the identified date. We ran different AI models, including BERT and Large Language Models (LLMs), to identify ADEs. ResultsWe annotated 1085 notes with 362 ADEs. These were used to train BERT models (precision or PPV: 0.584 ) and prompt-based LLM (0.563) but with improved sensitivity (0.725) and specificity (0.739) . ConclusionsIdentifying ADEs requires deep medical knowledge and simple Natural Language Processing models trying to identify ADEs only on the basis of language of encounter notes was not successful based on the BERT model performance. Prompt based models have deep knowledge but were trained with the objective of producing grammatically correct, coherent sentences, not with the medical knowledge required. LLMs also require significant computational power. ImplicationsWhile AI does have significant potential to advance our capacity for complex analytic tasks the current LLMs are not yet able to adequately identify ADEs in a corpus of electronic clinical notes.

Analysis of the current status and hot topics in spinal schwannoma imaging research based on bibliometrics

ObjectiveThis study aims to explore the current hot topics and future research trends in spinal schwannoma imaging research, providing a reference for related studies and promoting the development of spinal schwannoma imaging.MethodsWe conducted a literature search in the Web of Science database using the search terms (((TS = (Spinal schwannoma)) AND TS = (Imaging)) OR TS = (Spinal schwannoma)) AND TS = (image) to retrieve relevant articles. The collected data, including authors, keywords, journals, countries, institutions, and references, were subjected to visual analysis using the visualization software CiteSpace 6.4.2R and VOSviewer 1.6.19.ResultsA total of 310 relevant articles were identified. After further screening based on time limits, inclusion, and exclusion criteria, 179 articles were included in the study, consisting of 132 original articles and 42 reviews. These articles were authored by 1,034 authors from 35 countries and 324 institutions and were published in 82 different journals. The included articles cited a total of 6,583 references from 1,314 journals.ConclusionAlthough the field of spinal schwannoma imaging research is not a popular research area in the medical community, there has been an increasing international interest in this field in recent years. While China ranks high in terms of the number of published articles, there is still a gap in terms of the quality and research level compared to developed countries in Europe and America. MRI, as the gold standard for diagnosing spinal schwannomas, is expected to be a research hotspot in terms of feature analysis, enhancement characteristics, and quantitative analysis. It is also hoped that China can increase its investment in research and contribute to the field by publishing high-quality articles in the future.

Protein Expression, Amplification, and Mutation of HER2 Gene in Canine Primary Pulmonary Adenocarcinomas: Preliminary Results

Recently, human epidermal growth factor receptor 2 (HER2) has emerged as a therapeutic target of interest for non-small-cell lung cancer in humans. The role of HER2 in canine pulmonary adenocarcinomas is poorly documented. To address this gap, this study employed three methodologies: immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), and next-generation sequencing (NGS) to investigate the protein expression, gene amplification, and mutation of HER2 in 19 canine primary pulmonary adenocarcinomas. By IHC, 3 out of 19 cases were overexpressed 3+, 6 were 2+, and 10 were negative. With FISH, 2 cases were amplified (12.5%), 3 were inadequate for the analyses, and the others were non-amplified. With NGS, seven cases were inadequate. All other cases were wild-type, except for one IHC 3+ case, which was amplified with FISH and with a specific mutation already described in human pulmonary adenocarcinoma, V659E. This mutation is probably sensitive to tyrosine kinase inhibitory drugs. These results are similar to those in human medicine and to the few data in the literature on canine lung carcinomas; the presence of 12.5% of amplified cases in dogs lays the foundation for future targeted drugs against HER2 alterations.

Apple cider vinegar in folk and modern medicine: a historical review and current scientific evidence.

Introduction and Aim of the Study: Apple cider vinegar (ACV) has been used for centuries in various cultural and medical contexts globally. This study seeks to explore ACV's traditional and contemporary applications, evaluating scientific evidence for its efficacy and safety. Material and Methods: This review synthesizes information from PubMed, Google Scholar, and other scientific sources. Key search terms included "apple cider vinegar," "apple cider vinegar medical use," "apple cider vinegar antimicrobial effect," "apple cider vinegar lipid profile," "apple cider vinegar glucose level," "apple cider vinegar preparation," and "apple cider vinegar historical use." Results: Research supports ACV's traditional role as a disinfectant, demonstrating effectiveness against a range of bacteria and fungi. Both animal and human studies suggest ACV may help lower cholesterol and triglyceride levels while boosting HDL cholesterol. Evidence indicates ACV can enhance insulin sensitivity and reduce blood glucose levels, offering benefits for type 2 diabetes management. ACV shows antioxidant properties, potential for lowering blood pressure, and use in certain skin diseases, though further research is needed to confirm these effects. Conclusions: The historical use of ACV in traditional medicine finds partial support in modern scientific research. Its antimicrobial, lipid-modulating, and glucose-regulating properties are documented, suggesting potential benefits for cardiovascular health, weight management, and metabolic regulation. Nonetheless, the evidence for its cosmetic applications and impact on muscle cramps remains less definitive. Additional research involving larger, diverse populations is necessary to fully validate ACV's benefits and establish safe usage guidelines.

Exploring the Mechanisms that Allow Incompatible Beliefs to Coexist in the Cognitive System.

Despite assumptions that people strive for consistency between their beliefs, endorsement of mutually incompatible ones is not rare - a tendency we have previously labelled doublethink, by borrowing from Orwell. In an attempt to understand the nature of doublethink and the underlying mechanism that enables incompatible beliefs to coexist, we conducted two preregistered studies (total N = 691). To do so, in Study 1, we first explored how doublethink relates to (1) thinking styles (rational/intuitive, actively open-minded thinking, and need for cognitive closure), (2) a set of irrational beliefs (magical health, conspiratorial, superstitious, and paranormal beliefs) and (3) its predictiveness for questionable health practices (non-adherence to medical recommendations and use of traditional, complementary and alternative medicine). We then additionally expanded the set of health behaviors in Study 2, and related doublethink to trust in two epistemic authorities - science and the wisdom of the common man. Finally, in both studies, we explored whether those prone to inconsistent beliefs are also more likely to simultaneously rely on conventional and alternative medicine, despite their apparent incompatibility. While doublethink was positively related to need for cognitive closure and different irrational beliefs that easily incorporate contradictions, as well as negatively to actively open-minded thinking, we did not find it to be predictive of the use of non-evidence-based medicine nor of its simultaneous use with official medicine. It seems that this novel construct can be best understood as a feature of the cognitive system that allows incompatible claims to enter it. However, once beliefs are within the system, they are compartmentalized, without any cross-referencing between them. This is further reflected in non-evidence-based beliefs persisting within the belief system, irrespective of their content.

Enhancing Disease Detection in Electronic Medical Records: Integrating Human Expertise and Large Language Models with Application to Diabetes, Hypertension, and Acute Myocardial Infarction

ObjectiveElectronic medical records (EMR) are widely available to complement administrative data-based disease surveillance and healthcare performance evaluation. Defining conditions from EMR is labour-intensive, requiring advanced medical informatics knowledge, and is challenging without effective data extraction tools. This study developed a high-throughput pipeline to detect diseases in EMRs. MethodsWe developed a pipeline that leverages a generative large language model (LLM) to analyze, understand, and interpret EMR notes by following clinical experts’ designed prompts. The pipeline was applied to detect diabetes, hypertension, and acute myocardial infarction (AMI) from the EMRs for a cardiac patient cohort in Calgary, Canada. The performance was compared against clinician-validated diagnoses as the reference standard. ResultsThe cohort consisted of 3,413 patients with 551,095 clinical notes. The prevalence was 27.8%, 66.3%, and 54.3% for diabetes, hypertension, and AMI, respectively. The performance for detecting conditions varied: diabetes had 90.5% sensitivity, 83% specificity, and 67% positive predictive value (PPV); hypertension had 94.2% sensitivity, 30.2% specificity, and 73.8% PPV; and AMI had 86.4% sensitivity, 61% specificity, and 75.3% PPV. The monthly prevalence trends between the detected cases and reference standard showed similar patterns. ConclusionThe proposed pipeline demonstrated reasonable accuracy and high efficiency in disease detection without manually curated labels, indicating the potential for automated real-time disease surveillance using EMRs. ImplicationVariations of documentation practices in clinical note can impact the detection performance of different diseases. Hence, an automated pipeline integrating LLMs with expert knowledge may improve detection accuracy with reduced labour costs while indicating documentation quality.

The order of green and red LEDs irradiation affects the neural differentiation of human umbilical cord matrix-derived mesenchymal cells

Different wavelengths emitted from light-emitting diodes (LEDs) are known as an influential factor in proliferation and differentiation of various cell types. Since human umbilical cord matrix-derived mesenchymal cells (hUCMs) are ideal tools for human regenerative medicine clinical trials and stem cell researches, in the present study we investigated the neurogenesis effects of single and intermittent green and red LED irradiation on hUCM cells. Exposure of hUCMs to single and intermittent green (530 nm, 1.59 J/cm2) and red (630 nm, 0.318 J/cm2) lights significantly increased the expression of specific genes including nestin, β-tubulin III and Olig2. Additionally, immunocytochemical analysis confirmed the expression of specific neural-related proteins including nestin, β-tubulin III, Olig2 and GFAP. Also, alternating exposure of hUCM cells to green and red lights increased the expression of some neural markers more than either light alone. Further research are required to develop the application of LED irradiation as a useful tool for therapeutic purposes including neural repair and regeneration.

Heart failure in Europe: Guideline-directed medical therapy use and decision making in chronic and acute, pre-existing and de novo, heart failure with reduced, mildly reduced, and preserved ejection fraction-the ESC EORP Heart Failure III Registry.

We analysed baseline characteristics and guideline-directed medical therapy (GDMT) use and decisions in the European Society of Cardiology (ESC) Heart Failure (HF) III Registry. Between 1 November 2018 and 31 December 2020, 10 162 patients with acute HF (AHF, 39%, age 70 [62-79], 36% women) or outpatient visit for HF (61%, age 66 [58-75], 33% women), with HF with reduced (HFrEF, 57%), mildly reduced (HFmrEF, 17%) or preserved (HFpEF, 26%) ejection fraction were enrolled from 220 centres in 41 European or ESC-affiliated countries. With AHF, 97% were hospitalized, 2.2% received intravenous treatment in the emergency department, and 0.9% received intravenous treatment in an outpatient clinic. AHF was seen by most by a general cardiologist (51%) and outpatient HF most by a HF specialist (48%). A majority had been hospitalized for HF before, but 26% of AHF and 6.1% of outpatient HF had de novo HF. Baseline use, initiation and discontinuation of GDMT varied according to AHF versus outpatient HF, de novo versus pre-existing HF, and by ejection fraction. After the AHF event or outpatient HF visit, use of any renin-angiotensin system inhibitor, angiotensin receptor-neprilysin inhibitor, beta-blocker, mineralocorticoid receptor antagonist and loop diuretics was 89%, 29%, 92%, 78%, and 85% in HFrEF; 89%, 9.7%, 90%, 64%, and 81% in HFmrEF; and 77%, 3.1%, 80%, 48%, and 80% in HFpEF. Use and initiation of GDMT was high in cardiology centres in Europe, compared to previous reports from cohorts and registries including more primary care and general medicine and regions more local or outside of Europe and ESC-affiliated countries.

Clinical decision support system using hierarchical fuzzy diagnosis model for migraine and tension-type headache based on International Classification of Headache Disorders, 3rd edition

ObjectiveTo determine whether the diagnostic ability of the newly designed hierarchical fuzzy diagnosis method is consistent with that of headache experts for probable migraine (PM) and probable tension-type headache (PTTH).BackgroundClinical decision support systems (CDSS) are computer systems designed to help doctors to make clinician decisions by information technology, and have proven to be effective in improving headache diagnosis by making medical knowledge readily available to users in some studies. However, one serious drawback is that the CDSS lacks the ability to deal with some fuzzy boundaries of the headache features utilized in diagnostic criteria, which might be caused by patients’ recall bias and subjective bias.MethodsA hybrid mechanism of rule-based reasoning and hierarchical fuzzy diagnosis method based on International Classification of Headache Disorders, 3rd edition (ICHD-3) was designed and then validated by a retrospective study with 325 consecutive patients and a prospective study with 380 patients who were clinically diagnosed with migraine and TTH at the headache clinic of Chinese PLA General Hospital.ResultsThe results of the diagnostic test in the retrospective study indicated that the fuzzy-based CDSS can be used in the diagnosis of migraine without aura (MO) (sensitivity 97.71%, specificity 100%), TTH (sensitivity 98.57%, specificity 100%), PM (sensitivity 91.25%, specificity 98.75%) and PTTH (sensitivity 90.91%, specificity 99.63%). While in the prospective study, the diagnostic performances were MO (sensitivity 91.62%, specificity 96.52%), TTH (sensitivity 92.17%, specificity 95.47%), PM (sensitivity 85.48%, specificity 98.11%) and PTTH (sensitivity 87.50%, specificity 98.60%). Cohen’s kappa values for the consistency test were 0.984 ± 0.018 (MO), 0.991 ± 0.018 (TTH), 0.916 ± 0.051 (PM), 0.932 ± 0.059 (PTTH) in the retrospective study and 0.884 ± 0.047 (MO), 0.870 ± 0.055 (TTH), 0.853 ± 0.073 (PM), 0.827 ± 0.118 (PTTH) in the prospective study, which indicated good consistency with the fuzzy-based CDSS and the gold standard (p < 0.001).ConclusionWe developed a fuzzy-based CDSS performs much more similarly to expert diagnosis and performs better than the routine CDSS method in the diagnosis of migraine and TTH, and it could promote the application of artificial intelligence in the area of headache diagnosis.

Communication as Healing Art in Caring for Persons with Alzheimer's and Dementia.

Communication is an essential aspect of care for all patients but is especially important for individuals with Alzheimer's disease or other forms of dementia. Nurses who care for persons living with dementia (PwD), as well as family members and caregivers, need understanding of verbal and nonverbal communication techniques and approaches that are effective throughout the stages of dementia. Spiritual assessment and care also are critical. This article provides evidence-based and biblical examples for communication as a healing art in caring for PwD.

From Tutor to Future Educator: Investigating the Role of Peer-Peer Tutoring in Shaping Careers in Medical Education

AbstractIn the effort to promote academic excellence and provide teaching experiences and training for medical students, the University of California, Irvine, School of Medicine (UCISOM) built a novel peer tutoring program (2020), Collaborative Learning Communities with Medical Students as Teachers (CLC-MSAT). While the role of peer-assisted learning in student success on academic courses is well established, we wanted to assess the impact of our UCISOM peer-assisted learning program on tutors’ career interest in medical education. Through a mixed-methods analysis of our peer tutors’ experiences, we found 100% were overall satisfied with their positions; > 85% learned new skills; > 88% felt they were strong teachers; > 88% felt they now had a stronger grasp of the medical curriculum and most importantly, 100% of students maintained their interest and aspirations to serve in a future medical educator role after participating as a tutor. Additionally, we found a statistically significant relationship between tutors’ intentions of exploring a career in academic medicine after serving in the CLC program. Our findings suggest that our tutoring program has had a positive impact in providing authentic teaching experiences and training to medical students early in their careers at UCISOM, which may help prepare the next generation of qualified academic clinicians.

Antimicrobial Resistance of Staphylococcus spp. from Human Specimens Submitted to Diagnostic Laboratories in South Africa, 2012–2017

Antimicrobial resistance (AMR) poses a significant worldwide health challenge associated with prolonged illnesses, increased healthcare costs, and high mortality rates. The present study examined the patterns and predictors of AMR among human Staphylococcus isolates obtained from diagnostic laboratories in South Africa between 2012 and 2017. This study examined data from 404 217 isolates, assessing resistance rates across different characteristics such as age, sample origin, Staphylococcus species, and study period. The highest resistance was observed against cloxacillin (70.3%), while the lowest resistance was against Colistin (0.1%). A significant (p < 0.05) decreasing trend in AMR was observed over the study period, while a significant increasing temporal trend (p < 0.05) was observed for multidrug resistance (MDR) over the same period. A significant (p < 0.05) association was observed between specimen type, species of organism, and year of isolation with AMR outcome. Significant (p < 0.05) associations were observed between specimen type and season with MDR. The observed high levels of AMR and a growing trend in MDR are concerning for public health. Clinicians should take these findings into account when deciding on therapeutic options. Continued monitoring of AMR among Staphylococcus spp. and judicious use of antimicrobials in human medicine should be promoted.