PURPOSE OF REVIEW: Men face distinctive health-related challenges as a result of biological, behavioral, and sociocultural factors. In addition, the modern healthcare system does not offer men equal opportunities and options to ensure sex-specific access and delivery to health services. Men's health concerns are, indeed, often not addressed or even forgotten. In this review, we wanted to assess the impact of biology and sociocultural effects on sex-specific life-expectancy. RECENT FINDINGS: Globally, men have a shorter life expectancy than women. With a 5.8 years gender gap in the USA and 5.4 in the EU-27 (both in 2022). Cardiovascular disease, cancer, and accidents continue to represent the primary causes of mortality for both genders with all having disproportional preponderance in men. In recent years, there has been a notable decline in age-adjusted mortality rates related to cancer, while there has been an increase in deaths from accidental and intentional self-harm. Moreover, in the United States, men are more likely than women to develop and die from nonsex-specific cancers. As a result, men's poor health affects productivity, absenteeism, and employment. SUMMARY: The status of men in healthcare is complex. It is rooted in history, culture, and institutions. To address disparities, we need a comprehensive approach that includes policy reforms, sociocultural changes, and a fair and equitable public discourse. Grassroots and top-down strategies are needed to ensure a value-based societal healthcare system acknowledging the unique health needs of men.
- MeSH
- Healthcare Disparities statistics & numerical data MeSH
- Health Status Disparities MeSH
- Health Services Accessibility statistics & numerical data MeSH
- Humans MeSH
- Life Expectancy * MeSH
- Delivery of Health Care statistics & numerical data MeSH
- Health Equity MeSH
- Sex Factors MeSH
- Men's Health * MeSH
- Check Tag
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
- Geographicals
- United States MeSH
BACKGROUND: Various explicit screening tools, developed mostly in central Europe and the USA, assist clinicians in optimizing medication use for older adults. The Turkish Inappropriate Medication use in oldEr adults (TIME) criteria set, primarily based on the STOPP/START criteria set, is a current explicit tool originally developed for Eastern Europe and subsequently validated for broader use in Central European settings. Reviewed every three months to align with the latest scientific literature, it is one of the most up-to-date tools available. The tool is accessible via a free mobile app and website platforms, ensuring convenience for clinicians and timely integration of updates as needed. Healthcare providers often prefer to use their native language in medical practice, highlighting the need for prescribing tools to be translated and adapted into multiple languages to promote optimal medication practices. OBJECTIVE: To describe the protocol for cross-cultural and language validation of the TIME criteria in various commonly used languages and to outline its protocol for clinical validation across different healthcare settings. METHODS: The TIME International Study Group comprised 24 geriatric pharmacotherapy experts from 12 countries. In selecting the framework for the study, we reviewed the steps and outcomes from previous research on cross-cultural adaptations and clinical validations of explicit tools. Assessment tools were selected based on both their validity in accurately addressing the relevant issues and their feasibility for practical implementation. The drafted methodology paper was circulated among the study group members for feedback and revisions leading to a final consensus. RESULTS: The research methodology consists of two phases. Cross-cultural adaptation/language validation phase follows the 8-step approach recommended by World Health Organization. This phase allows regions or countries to make modifications to existing criteria or introduce new adjustments based on local prescribing practices and available medications, as long as these adjustments are supported by current scientific evidence. The second phase involves the clinical validation, where participants will be randomized into two groups. The control group will receive standard care, while the intervention group will have their treatment evaluated by clinicians who will review the TIME criteria and consider its recommendations. A variety of patient outcomes (i.e., number of hospital admissions, quality of life, number of regular medications [including over the counter medications], geriatric syndromes and mortality) in different healthcare settings will be investigated. CONCLUSION: The outputs of this methodological report are expected to promote broader adoption of the TIME criteria. Studies building on this work are anticipated to enhance the identification and management of inappropriate medication use and contribute to improved patient outcomes.
BACKGROUND: Isolated injury to the superior mesenteric vein (SMV) caused by blunt abdominal trauma is rare but often lethal, especially in pediatric patients. Due to the low incidence of SMV injuries, there are no universal guidelines for its diagnosis and treatment. The diagnosis is made using either computed tomography (CT) or intraoperative exploration. Primary vascular repair is recommended. CASE REPORT: A 10-year-old girl was transferred to a trauma center after a high-energy motor vehicle collision. Under the diagnosis of acute abdomen with hemoperitoneum, the patient underwent urgent laparotomy, 34 min after admission to the hospital. A complete laceration of the SMV trunk was observed. Definitive vascular repair of the transected SMV was performed. An interposition graft from the internal jugular vein was used with a good postoperative course. CONCLUSION: This case report demonstrates that definitive vascular repair of the SMV reduces the risk of intestinal ischemia and should be performed in cases where ligation presents a real threat to small bowel viability. In cases of severe SMV injury, the internal jugular vein is a high-quality and easily accessible graft.
- MeSH
- Child MeSH
- Accidents, Traffic MeSH
- Lacerations * surgery etiology MeSH
- Laparotomy methods MeSH
- Humans MeSH
- Tomography, X-Ray Computed MeSH
- Abdominal Injuries * complications surgery diagnosis MeSH
- Vascular System Injuries * surgery etiology diagnosis MeSH
- Wounds, Nonpenetrating * complications surgery diagnosis MeSH
- Mesenteric Veins * injuries surgery diagnostic imaging MeSH
- Vascular Surgical Procedures methods MeSH
- Check Tag
- Child MeSH
- Humans MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Case Reports MeSH
BACKGROUND: Advances in paediatric type 1 diabetes management and increased use of diabetes technology have led to improvements in glycaemia, reduced risk of severe hypoglycaemia, and improved quality of life. Since 1993, progressively lower HbA1c targets have been set. The aim of this study was to perform a longitudinal analysis of HbA1c, treatment regimens, and acute complications between 2013 and 2022 using data from eight national and one international paediatric diabetes registries. METHODS: In this longitudinal analysis, we obtained data from the Australasian Diabetes Data Network, Czech National Childhood Diabetes Register, Danish Registry of Childhood and Adolescent Diabetes, Diabetes Prospective Follow-up Registry, Norwegian Childhood Diabetes Registry, England and Wales' National Paediatric Diabetes Audit, Swedish Childhood Diabetes Registry, T1D Exchange Quality Improvement Collaborative, and the SWEET initiative. All children (aged ≤18 years) with type 1 diabetes with a duration of longer than 3 months were included. Investigators compared data from 2013 to 2022; analyses performed on data were pre-defined and conducted separately by each respective registry. Data on demographics, HbA1c, treatment regimen, and event rates of diabetic ketoacidosis and severe hypoglycaemia were collected. ANOVA was performed to compare means between registries and years. Joinpoint regression analysis was used to study significant breakpoints in temporal trends. FINDINGS: In 2022, data were available for 109 494 children from the national registries and 35 590 from SWEET. Between 2013 and 2022, the aggregated mean HbA1c decreased from 8·2% (95% CI 8·1-8·3%; 66·5 mmol/mol [65·2-67·7]) to 7·6% (7·5-7·7; 59·4mmol/mol [58·2-60·5]), and the proportion of participants who had achieved HbA1c targets of less than 7% (<53 mmol/mol) increased from 19·0% to 38·8% (p<0·0001). In 2013, the aggregate event rate of severe hypoglycaemia rate was 3·0 events per 100 person-years (95% CI 2·0-4·9) compared with 1·7 events per 100 person-years (1·0-2·7) in 2022. In 2013, the aggregate event rate of diabetic ketoacidosis was 3·1 events per 100 person-years (95% CI 2·0-4·8) compared with 2·2 events per 100 person-years (1·4-3·4) in 2022. The proportion of participants with insulin pump use increased from 42·9% (95% CI 40·4-45·5) in 2013 to 60·2% (95% CI 57·9-62·6) in 2022 (mean difference 17·3% [13·8-20·7]; p<0·0001), and the proportion of participants using continuous glucose monitoring (CGM) increased from 18·7% (95% CI 9·5-28·0) in 2016 to 81·7% (73·0-90·4) in 2022 (mean difference 63·0% [50·3-75·7]; p<0·0001). INTERPRETATION: Between 2013 and 2022, glycaemic outcomes have improved, parallel to increased use of diabetes technology. Many children had HbA1c higher than the International Society for Pediatric and Adolescent Diabetes (ISPAD) 2022 target. Reassuringly, despite targeting lower HbA1c, severe hypoglycaemia event rates are decreasing. Even for children with type 1 diabetes who have access to specialised diabetes care and diabetes technology, further advances in diabetes management are required to assist with achieving ISPAD glycaemic targets. FUNDING: None. TRANSLATIONS: For the Norwegian, German, Czech, Danish and Swedish translations of the abstract see Supplementary Materials section.
- MeSH
- Diabetes Mellitus, Type 1 * epidemiology blood drug therapy MeSH
- Child MeSH
- Glycated Hemoglobin * analysis MeSH
- Hypoglycemia epidemiology MeSH
- Hypoglycemic Agents * therapeutic use MeSH
- Infant MeSH
- Blood Glucose * analysis MeSH
- Humans MeSH
- Longitudinal Studies MeSH
- Adolescent MeSH
- Child, Preschool MeSH
- Registries * statistics & numerical data MeSH
- Glycemic Control statistics & numerical data methods MeSH
- Treatment Outcome MeSH
- Check Tag
- Child MeSH
- Infant MeSH
- Humans MeSH
- Adolescent MeSH
- Male MeSH
- Child, Preschool MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
INTRODUCTION: The histopathological classification for antineutrophil cytoplasmic autoantibody (ANCA)-associated glomerulonephritis (ANCA-GN) is a well-established tool to reflect the variety of patterns and severity of lesions that can occur in kidney biopsies. It was demonstrated previously that deep learning (DL) approaches can aid in identifying histopathological classes of kidney diseases; for example, of diabetic kidney disease. These models can potentially be used as decision support tools for kidney pathologists. Although they reach high prediction accuracies, their "black box" structure makes them nontransparent. Explainable (X) artificial intelligence (AI) techniques can be used to make the AI model decisions accessible for human experts. We have developed a DL-based model, which detects and classifies the glomerular lesions according to the Berden classification. METHODS: Kidney biopsy slides of 80 patients with ANCA-GN from 3 European centers, who underwent a diagnostic kidney biopsy between 1991 and 2011, were included. We also investigated the explainability of our model using Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps. These maps were analyzed by pathologists to compare the decision-making criteria of humans and the DL model and assess the impact of different training settings. RESULTS: The DL model shows a prediction accuracy of 93% for classifying lesions. The heatmaps from our trained DL models showed that the most predictive areas in the image correlated well with the areas deemed to be important by the pathologist. CONCLUSION: We present the first DL-based computational pipeline for classifying ANCA-GN kidney biopsies as per the Berden classification. XAI techniques helped us to make the decision-making criteria of the DL accessible for renal pathologists, potentially improving clinical decision-making.
- Publication type
- Journal Article MeSH
PURPOSE OF REVIEW: A critical evaluation of contemporary literature regarding the role of big data, artificial intelligence, and digital technologies in precision cardio-oncology care and survivorship, emphasizing innovative and groundbreaking endeavors. RECENT FINDINGS: Artificial intelligence (AI) algorithm models can automate the risk assessment process and augment current subjective clinical decision tools. AI, particularly machine learning (ML), can identify medically significant patterns in large data sets. Machine learning in cardio-oncology care has great potential in screening, diagnosis, monitoring, and managing cancer therapy-related cardiovascular complications. To this end, large-scale imaging data and clinical information are being leveraged in training efficient AI algorithms that may lead to effective clinical tools for caring for this vulnerable population. Telemedicine may benefit cardio-oncology patients by enhancing healthcare delivery through lowering costs, improving quality, and personalizing care. Similarly, the utilization of wearable biosensors and mobile health technology for remote monitoring holds the potential to improve cardio-oncology outcomes through early intervention and deeper clinical insight. Investigations are ongoing regarding the application of digital health tools such as telemedicine and remote monitoring devices in enhancing the functional status and recovery of cancer patients, particularly those with limited access to centralized services, by increasing physical activity levels and providing access to rehabilitation services. SUMMARY: In recent years, advances in cancer survival have increased the prevalence of patients experiencing cancer therapy-related cardiovascular complications. Traditional cardio-oncology risk categorization largely relies on basic clinical features and physician assessment, necessitating advancements in machine learning to create objective prediction models using diverse data sources. Healthcare disparities may be perpetuated through AI algorithms in digital health technologies. In turn, this may have a detrimental effect on minority populations by limiting resource allocation. Several AI-powered innovative health tools could be leveraged to bridge the digital divide and improve access to equitable care.
- Publication type
- Journal Article MeSH
BACKGROUND: Robot-assisted minimally invasive esophagectomy (RAMIE) is increasingly adopted in centers worldwide, with ongoing refinements to enhance results. This study aims to assess the current state of RAMIE worldwide and to identify potential areas for improvement. METHODS: This descriptive study analyzed prospective data from esophageal cancer patients who underwent transthoracic RAMIE in Upper GI International Robotic Association (UGIRA) centers. Main endpoints included textbook outcome rate, surgical techniques, and perioperative outcomes. Analyses were performed separately for intrathoracic (Ivor-Lewis) and cervical anastomosis (McKeown), divided into three time cohorts (2016-2018, 2019-2020, 2021-2023). A sensitivity analysis was conducted with cases after the learning curve (> 70 cases). RESULTS: Across 28 UGIRA centers, 2012 Ivor-Lewis and 1180 McKeown procedures were performed. Over the time cohorts, textbook outcome rates were 39%, 48%, and 49% for Ivor-Lewis, and 49%, 63%, and 61% for McKeown procedures, respectively. Fully robotic procedures accounted for 66%, 51%, and 60% of Ivor-Lewis procedures, and 53%, 81%, and 66% of McKeown procedures. Lymph node yield showed 27, 30, and 30 nodes in Ivor-Lewis procedures, and 26, 26, and 34 nodes in McKeown procedures. Furthermore, high mediastinal lymphadenectomy was performed in 65%, 43%, and 37%, and 70%, 48%, and 64% of Ivor-Lewis and McKeown procedures, respectively. Anastomotic leakage rates were 22%, 22%, and 16% in Ivor-Lewis cases, and 14%, 12%, and 11% in McKeown cases. Hospital stay was 13, 14, and 13 days for Ivor-Lewis procedures, and 12, 9, and 11 days for McKeown procedures. In Ivor-Lewis and McKeown, respectively, the sensitivity analysis revealed textbook outcome rates of 43%, 54%, and 51%, and 47%, 64%, and 64%; anastomotic leakage rates of 28%, 18%, and 15%, and 13%, 11%, and 10%; and hospital stay of 11, 12, and 12 days, and 10, 9, and 9 days. CONCLUSIONS: This study demonstrates favorable outcomes over time in achieving textbook outcome after RAMIE. Areas for improvement include a reduction of anastomotic leakage and shortening of hospital stay.
- MeSH
- Esophagectomy * methods MeSH
- Middle Aged MeSH
- Humans MeSH
- Minimally Invasive Surgical Procedures methods MeSH
- Esophageal Neoplasms * surgery pathology MeSH
- Follow-Up Studies MeSH
- Postoperative Complications epidemiology MeSH
- Prognosis MeSH
- Prospective Studies MeSH
- Registries * MeSH
- Robotic Surgical Procedures * methods MeSH
- Aged MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
The present study has undertaken the isolation of marine yeasts from mangrove sediment samples and their ability to produce alkaline protease enzymes. A total of 14 yeast isolates were recovered on yeast-malt agar (YMA) and yeast extract peptone dextrose (YEPD) agar medium. After screening for proteolytic activity on skim milk agar, marine yeast isolate, AKB-1 exhibited a hydrolysis zone of 18 mm. Optimal conditions for the enzyme production from yeast isolate AKB-1 were at 30 °C, pH 8, fructose as carbon source, potassium nitrate as nitrogen source, and 25% saline concentration. Under the optimal conditions, the protease enzyme activity of the isolate AKB-1 was observed to be 978 IU/mL. The structural and functional analysis was carried out through FTIR and HPLC analysis for the extracted protease enzyme. Furthermore, the enzyme produced was partially purified by solvent extraction using ethyl acetate and ammonium sulfate precipitation (3.4-fold) followed by dialysis (56.8-fold). The molecular weight of the purified enzyme was observed to be around 60 kDa using SDS-PAGE. The extracted protein showed good antibacterial activity against six different clinical bacterial pathogens and the highest against Bacillus cereus (16 ± 0.5 mm). The extracted protease enzyme was revealed to remove blood stains from cloth within 20 min of application similar to the commercial detergent. The marine yeast isolate was further identified as Candida orthopsilosis AKB-1 (Accession number KY348766) through 18S rRNA sequencing, and a phylogenetic tree was generated.
- MeSH
- Anti-Bacterial Agents pharmacology metabolism chemistry isolation & purification MeSH
- Bacillus cereus drug effects MeSH
- Bacterial Proteins * chemistry pharmacology metabolism isolation & purification MeSH
- Candida * enzymology isolation & purification genetics classification MeSH
- Endopeptidases * chemistry metabolism isolation & purification pharmacology MeSH
- Phylogeny MeSH
- Geologic Sediments microbiology MeSH
- Hydrogen-Ion Concentration MeSH
- Culture Media chemistry MeSH
- Microbial Sensitivity Tests MeSH
- Molecular Weight MeSH
- Enzyme Stability MeSH
- Temperature MeSH
- Publication type
- Journal Article MeSH
OBJECTIVE: To evaluate the role of clinical exchange programs in postgraduate obgyn training using the International Federation of Gynecology and Obstetrics (FIGO)-World Association of Trainees in Obstetrics and Gynecology (WATOG) One World Exchange (OWE), a clinical exchange program held in France in October 2023, as a case-study. METHODS: This was a cross-sectional study. A 31-item structured questionnaire designed with Google Forms was electronically distributed to the 51 obgyn postgraduate trainees (OWE fellows) who participated in the OWE, to collect information about the exchange. Collected data was analyzed using IBM Statistical Product and Service Solutions (SPSS) Statistics for Windows. RESULTS: The survey response rate was 68.6%. The mean age of the respondents was 33.0 ± 4.0 years. Majority of the them were females (26, 74.3%), married (19, 54.3%), at least in their third year of training (30, 85.7%) and from Africa (11, 31.4%). During the period of the exchange program, fellows observed various obstetric and gynecologic procedures, including open and minimal access procedures, with more than one-fifth (8, 22.9%) of them reporting that they were allowed to assist in some of these procedures. The fellows noted salient differences in practice between their exchange hospitals and their home countries. An overwhelming majority (30, 85.7%) of the fellows believed the OWE was beneficial and would positively impact their clinical practices back in their home countries. CONCLUSION: Clinical exchange programs like the OWE provide valuable benefits in improving the clinical knowledge and skills of postgraduate obgyn trainees.
- MeSH
- Adult MeSH
- Gynecology * education MeSH
- Humans MeSH
- International Educational Exchange * MeSH
- Obstetrics * education MeSH
- Cross-Sectional Studies MeSH
- Surveys and Questionnaires MeSH
- Societies, Medical MeSH
- Education, Medical, Graduate * methods MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- France MeSH
BACKGROUND: As the healthcare sector evolves, Artificial Intelligence's (AI's) potential to enhance laboratory medicine is increasingly recognized. However, the adoption rates and attitudes towards AI across European laboratories have not been comprehensively analyzed. This study aims to fill this gap by surveying European laboratory professionals to assess their current use of AI, the digital infrastructure available, and their attitudes towards future implementations. METHODS: We conducted a methodical survey during October 2023, distributed via EFLM mailing lists. The survey explored six key areas: general characteristics, digital equipment, access to health data, data management, AI advancements, and personal perspectives. We analyzed responses to quantify AI integration and identify barriers to its adoption. RESULTS: From 426 initial responses, 195 were considered after excluding incomplete and non-European entries. The findings revealed limited AI engagement, with significant gaps in necessary digital infrastructure and training. Only 25.6 % of laboratories reported ongoing AI projects. Major barriers included inadequate digital tools, restricted access to comprehensive data, and a lack of AI-related skills among personnel. Notably, a substantial interest in AI training was expressed, indicating a demand for educational initiatives. CONCLUSIONS: Despite the recognized potential of AI to revolutionize laboratory medicine by enhancing diagnostic accuracy and efficiency, European laboratories face substantial challenges. This survey highlights a critical need for strategic investments in educational programs and infrastructure improvements to support AI integration in laboratory medicine across Europe. Future efforts should focus on enhancing data accessibility, upgrading technological tools, and expanding AI training and literacy among professionals. In response, our working group plans to develop and make available online training materials to meet this growing educational demand.
- MeSH
- Laboratories, Clinical MeSH
- Humans MeSH
- Surveys and Questionnaires MeSH
- Artificial Intelligence * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Europe MeSH
