🌟 Most Cited Author Award | Global Recognition for Research Excellence | #PencisAwards #MostCitedAuthor

INTRODUCTION

The Most Cited Author Award stands as a global testament to research excellence, awarded to individuals whose scholarly work has amassed exceptional citations and influence. Hosted by Pencis Conferences, this recognition is rooted in scientific credibility and real-world impact. It acknowledges the authors whose publications not only advance knowledge but shape global research trends across disciplines such as infectious diseases, life sciences, and healthcare. This prestigious award emphasizes how vital consistent, innovative, and referenced work is to the progress of academic thought and real-world solutions. Through this award, leading scholars gain deserved visibility and acclaim while inspiring the next generation of researchers to pursue groundbreaking paths. It marks a pinnacle of research leadership and collaborative potential.

RESEARCH IMPACT AND CITATION EXCELLENCE

High citation metrics are more than just numbers—they represent the tangible influence of a scholar's work on their field and beyond. The Most Cited Author Award places emphasis on research that is not only prolific but profoundly impactful in shaping methodologies, knowledge dissemination, and future studies. Citation indexes and H-indices help to quantify how research is received, reused, and built upon. This award showcases how such scholarship transforms theoretical knowledge into real-world change, especially in fields like infectious diseases and public health, where timely insights save lives and inform policy.

GLOBAL SCHOLARLY RECOGNITION

Academic visibility on a global platform is key to advancing collaborative science. The Most Cited Author Award acts as an international hallmark, highlighting researchers whose work resonates across borders and cultures. By being recognized at this level, scholars amplify their reach, gain access to prestigious journals, and open doors to global partnerships. As science becomes increasingly interdisciplinary and cross-national, such recognition helps integrate ideas across continents, from high-impact healthcare solutions to life-saving infectious disease control strategies.

ACADEMIC LEADERSHIP AND REPUTATION

This award isn't just about numbers—it honors the intellectual leadership of researchers who guide entire fields with their vision and rigor. Being the Most Cited Author reflects a combination of scholarly excellence, research foresight, and community influence. It helps establish a legacy within the academic ecosystem, positioning researchers as role models and thought leaders. Such a status enhances opportunities for keynote roles, editorial board positions, and greater responsibility in shaping future research agendas.

INNOVATION AND COLLABORATIVE POTENTIAL

The recognition also shines a light on innovation—how novel methodologies, bold hypotheses, and new research frameworks redefine knowledge boundaries. The Most Cited Author Award recognizes these pioneering efforts and boosts researchers’ chances of collaborating across industries, academic institutions, and international bodies. Innovation often thrives when interdisciplinary minds converge, and awardees find themselves at the heart of these transformative intersections, especially in emerging areas of health and biosciences.

MOTIVATION FOR EMERGING SCHOLARS

Beyond honoring established researchers, this award serves as a powerful source of inspiration for early-career scientists and students. It symbolizes what’s possible through persistence, quality, and scholarly dialogue. Young researchers observing these awardees are encouraged to contribute meaningfully, stay consistent, and think globally. The visibility of this recognition encourages institutions to invest in talent, support research funding, and promote international collaboration in high-stakes fields such as infectious diseases.

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Hashtags

#MostCitedAuthor, #PencisConferences, #InfectiousDiseasesResearch, #GlobalResearchImpact, #AcademicLeadership, #CitationExcellence, #InnovationInScience, #ResearchRecognition, #HindexLeaders, #TopResearchers, #ScholarlyInfluence, #HealthScienceResearch, #LifeSciencesAwards, #AcademicVisibility, #ResearchInspiration, #CrossBorderScience, #InternationalCollaboration, #ScientificInnovation, #PeerRecognition, #PencisAwards,

Genotypic vs Phenotypic Detection of MDR-TB & XDR-TB | #Tuberculosis #Pencis

                                                                  

INTRODUCTION

Drug-resistant tuberculosis (DR-TB) continues to be a global public health concern, requiring rapid and accurate diagnostic strategies to combat its progression and transmission. Among these, the comparison of phenotypic and genotypic methods for drug susceptibility testing (DST) is crucial for understanding their respective strengths and limitations. This study assesses both phenotypic resistance using Löwenstein–Jensen medium and genotypic resistance using the GenoType MTBDRplus assay in Mycobacterium tuberculosis isolates. It evaluates resistance patterns to both first-line and second-line anti-TB drugs, including isoniazid (INH), rifampicin (RIF), streptomycin (STR), ethambutol (EMB), fluoroquinolones, and aminoglycosides. The results emphasize the growing burden of multidrug-resistant (MDR) and extensively drug-resistant tuberculosis (XDR-TB), underscoring the need for diagnostic approaches that combine speed and reliability. While genotypic testing enables rapid detection, certain phenotypically resistant strains remain undetected, suggesting a diagnostic gap. This calls for an integrative diagnostic framework that combines molecular assays and phenotypic methods for robust MDR-TB detection and management.

DRUG RESISTANCE PATTERNS IN TUBERCULOSIS

Understanding drug resistance patterns in M. tuberculosis is vital for tailoring effective treatment regimens. This study identified high levels of resistance to INH (84.85%) and significant resistance to RIF (46.97%), which are two cornerstone drugs in first-line TB therapy. Notably, nearly half of the isolates also showed resistance to STR (48.48%) and EMB (30.30%). These resistance rates illustrate the ongoing challenge of treating TB with standard regimens and highlight the urgent need for routine susceptibility testing, especially in regions with a high burden of MDR-TB. Such data not only inform clinicians but also shape public health strategies for TB control.

GENOTYPIC VERSUS PHENOTYPIC TESTING

Genotypic methods such as the GenoType MTBDRplus offer speed and specificity in detecting mutations associated with drug resistance, particularly for INH and RIF. This study found high concordance between genotypic and phenotypic results—95.16% for INH and 94.74% for RIF. However, discordant cases underscore a limitation of molecular methods, especially when resistance is due to novel or uncommon mutations not covered by the assay. Phenotypic testing remains essential for detecting such cases. Therefore, the integration of both testing modalities enhances diagnostic precision and ensures that resistant strains are not missed, thus supporting more effective treatment planning.

CHARACTERIZATION OF MDR AND XDR-TB STRAINS

Multidrug-resistant tuberculosis is defined by resistance to both INH and RIF, while extensively drug-resistant TB includes additional resistance to fluoroquinolones and aminoglycosides. In this study, 29 MDR-TB isolates were identified, of which 41.37% were resistant to fluoroquinolones and 31.03% to both fluoroquinolones and aminoglycosides—thus meeting XDR-TB criteria. These findings indicate a disturbing trend in the emergence of highly resistant TB strains that are increasingly difficult to treat. Identifying these resistance profiles early is crucial for administering second-line therapies effectively and avoiding therapeutic failure, further emphasizing the need for advanced DST systems.

ROLE OF SEQUENCING IN TB DIAGNOSTICS

While current genotypic assays are effective in detecting common resistance mutations, whole-genome sequencing (WGS) offers a more comprehensive view of resistance mechanisms. Cases in this study where phenotypic resistance was not matched by genotypic results suggest the presence of mutations not targeted by the GenoType MTBDRplus assay. Integrating WGS can help bridge this diagnostic gap by identifying novel mutations and providing insights into the evolutionary pathways of resistance. Sequencing could ultimately become the gold standard for MDR/XDR-TB diagnostics, especially when combined with rapid assays for initial screening.

IMPLICATIONS FOR TB CONTROL STRATEGIES

The findings of this study have significant implications for public health policy and TB management programs. The coexistence of high phenotypic resistance with partial genotypic detection underlines the need for dual diagnostic approaches in TB-endemic regions. Surveillance programs should incorporate both molecular and culture-based methods to improve case detection and reduce transmission. Additionally, routine resistance profiling for all confirmed TB cases will enable early identification of MDR/XDR forms, ensuring timely initiation of appropriate treatment and reducing the likelihood of further drug resistance amplification.

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HASHTAGS

#TuberculosisDiagnosis, #MDRTB, #XDRTB, #DrugResistance, #MTBDRplus, #PhenotypicTesting, #GenotypicTesting, #FluoroquinoloneResistance, #TBResearch, #DrugSusceptibilityTesting, #RapidTBDetection, #INHResistance, #RIFResistance, #MolecularDiagnostics, #MycobacteriumTuberculosis, #TBSequencing, #WholeGenomeSequencing, #PublicHealthTB, #TBControlStrategies, #InfectiousDiseasesResearch,

Predicting EGFR Mutation in NSCLC Using CT, Radiomics & AI | #LungCancer #Pencis

INTRODUCTION

Non-small cell lung cancer (NSCLC), particularly adenocarcinoma, is a molecularly diverse disease where EGFR mutations serve as critical biomarkers for guiding targeted therapies, especially tyrosine kinase inhibitors (TKIs). In clinical settings, the rapid and non-invasive identification of EGFR mutation status is essential for initiating precision treatment. This study addresses that need by developing a nomogram combining the most informative clinical, CT, and radiomic features to predict EGFR mutation status. By using real-world retrospective data from 521 histologically confirmed NSCLC adenocarcinoma patients, the study creates a reliable tool to support early therapeutic decisions. The research demonstrates how integrating multi-modal data and machine learning can improve clinical decision-making without depending solely on invasive biopsies.

DATA ACQUISITION AND STUDY POPULATION

The study involved 521 NSCLC adenocarcinoma patients who underwent CT imaging along with either surgical resection or biopsy for EGFR mutation testing. This real-world, retrospective dataset ensures that the results are clinically applicable and mirror actual diagnostic scenarios. Patient demographics and medical records were examined to extract the most relevant clinical data, and imaging data were processed to extract detailed CT and radiomic features. This diverse dataset forms the foundation for developing and validating the predictive models and reflects the heterogeneity typically encountered in clinical practice.

FEATURE EXTRACTION AND SELECTION STRATEGY

Three major feature types were collected: clinical variables (e.g., age, sex), CT-based morphological features, and radiomic features derived from regions of interest (ROIs) in the CT scans. Radiomic analysis allowed the quantification of tumor heterogeneity and textural complexity beyond visual assessment. A feature preselection process was employed to identify 11 key signatures (2 clinical, 2 CT-based, and 7 radiomic), which proved to have the highest predictive value. This step minimized redundancy, reduced dimensionality, and enhanced the interpretability and generalizability of the final model.

MACHINE LEARNING MODEL DEVELOPMENT

To evaluate the predictive power of different feature combinations, five Random Forest classifiers were trained on various datasets. These datasets ranged from raw unfiltered data to optimized subsets with only the most relevant features. Among all configurations, the model trained exclusively on the preselected 11 features showed the highest performance metrics. Random Forest, a robust ensemble-based learning algorithm, was selected for its ability to handle feature heterogeneity and complex interactions, making it well-suited for integrating clinical and imaging data.

RESULTS AND PERFORMANCE METRICS

The optimized Random Forest model yielded superior predictive performance across all key evaluation metrics. It achieved an AUC of 0.88, precision of 0.90, recall of 0.94, F1-score of 0.91, and overall accuracy of 0.87. Both macro- and micro-average scores exceeded 0.89, underscoring the model’s strong classification capabilities for distinguishing EGFR-mutant from wild-type NSCLC cases. These results indicate that the selected multi-modal features effectively capture the biological and morphological signatures associated with EGFR mutation, validating the approach for clinical use.

CLINICAL IMPLICATIONS AND CONCLUSION

The developed nomogram offers a robust, non-invasive tool for the early prediction of EGFR mutation status in NSCLC adenocarcinoma patients. It supports precision treatment planning by enabling rapid identification of candidates for TKI therapy without the delay or risk associated with biopsy-based molecular testing. This approach bridges imaging and molecular biology through machine learning, promoting personalized care. Future research may involve prospective validation and integration into clinical workflows to maximize real-world utility and improve outcomes in lung cancer management.

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Hashtags:

#EGFRMutation, #NSCLC, #LungCancer, #Radiomics, #MachineLearning, #CTImaging, #PrecisionMedicine, #TyrosineKinaseInhibitors, #CancerDiagnostics, #NonInvasiveTesting, #ClinicalAI, #RandomForest, #MedicalAI, #CancerPrediction, #DeepRadiomics, #EGFRStatus, #OncologyResearch, #NomogramModel, #ImageAnalysis, #PencisConference,

T Cell Dynamics in COVID-19 & Long COVID Recovery | Immune Insight 🔬 #LongCOVID #TCells #pencis

INTRODUCTION

SARS-CoV-2, the virus responsible for COVID-19, primarily affects the respiratory system, yet its impact on T cell homeostasis is profound and far-reaching. Beyond acute infection, a subset of patients experiences persistent immunological disturbances collectively termed long COVID (LC). This study investigates how T cell immunity is altered during acute COVID-19 and long COVID by analyzing T cell receptor (TCR) formation via T-cell receptor excision circle (TREC) quantification. We collected 231 peripheral blood samples from diverse cohorts, including patients with acute COVID-19, recovered individuals, long COVID sufferers, and healthy volunteers. Using flow cytometry, we evaluated the distribution of CD4+ and CD8+ T cell subpopulations, along with TREC levels. The aim was to understand how SARS-CoV-2 reshapes T cell compartments, especially the balance of naïve, memory, and effector phenotypes. Our study reveals that COVID-19 and LC are associated with persistent immune remodeling, with implications for immune competence, recovery, and autoimmunity. These findings may help unravel the immunological underpinnings of prolonged symptoms and guide therapeutic strategies aimed at immune reconstitution.

IMMUNE CELL PROFILING IN COVID-19

Through advanced flow cytometry, we conducted a comprehensive assessment of CD4+ and CD8+ T cell subpopulations in patients across four distinct cohorts. Our profiling included subsets such as naïve, central memory (CM), effector memory (EM), and terminally differentiated effector memory (TEMRA) cells, along with functional subsets like Th1, Th2, Th17, Tfh, Tc1, Tc2, Tc17, and Tc17.1. Notably, acute COVID-19 patients exhibited a shift from naïve T cells to central memory and Th2/Tc2 polarization, alongside a decline in effector memory cells. Long COVID patients, on the other hand, showed a resurgence of naïve cytotoxic T lymphocytes (CTLs) and continued skewing toward Th2 dominance, indicating immune deviation potentially linked to chronic antigen presence or immunopathology. These alterations imply that SARS-CoV-2 not only impacts the immune response during acute infection but also causes enduring imbalances that could underlie LC symptoms.

TREC LEVELS AS A BIOMARKER OF THYMIC FUNCTION

T-cell receptor excision circles (TRECs) serve as a surrogate marker of thymic output and new T cell generation. In our study, TREC levels positively correlated with the abundance of naïve T cells, particularly in the long COVID group. This finding suggests residual thymic activity even in the post-acute phase and points toward ongoing attempts by the immune system to restore homeostasis. Interestingly, while naïve T cells were reduced in acute COVID-19, they appeared to rebound in LC, potentially due to compensatory thymic regeneration. These patterns underline the importance of TREC as a dynamic biomarker of immune recovery and a possible predictor of long-term immune competence following SARS-CoV-2 infection.

TH2 AND TH17 POLARIZATION IN LONG COVID

A defining immunological signature observed in long COVID patients was the skewing of helper T cells towards Th2 and Th17 subsets. This polarization diverges from a typical antiviral Th1-dominated response and suggests a shift toward immune profiles associated with allergy and autoimmunity. The predominance of Th2-type cytokines and depletion of Tc1/Tc2/EM subsets indicates an imbalance that may reflect chronic immune activation, inappropriate regulation, or an underlying autoimmune trigger. These findings support theories that long COVID involves elements of immune dysregulation, including loss of tolerance, persistent inflammation, and possibly molecular mimicry, which may explain prolonged and systemic symptoms in LC patients.

MEMORY T CELL DYNAMICS POST-INFECTION

Memory T cell compartments, especially central memory (CM) and effector memory (EM) cells, exhibited differential regulation across COVID-19 disease stages. Acute infection led to increased CM T cells and decreased EM1, EM3, and TEMRA populations, suggesting a redistribution or functional exhaustion of effector memory subsets. In contrast, long COVID patients showed depletion of Em2, 3, and 4 subsets, indicating impaired recall responses or altered tissue homing behavior. These changes point toward a lingering disruption in immunological memory, which may affect both antiviral defense and vaccine responsiveness in LC individuals. Understanding these shifts is crucial for developing immune-targeted interventions.

IMPLICATIONS FOR IMMUNE RECONSTITUTION AND AUTOIMMUNITY

The persistent alterations in T cell profiles observed in both acute and long COVID cases emphasize the complexity of immune reconstitution post-SARS-CoV-2 infection. The reappearance of naïve T cells and increased TREC levels in LC may reflect thymic rebound, yet the accompanying Th2/Th17 bias raises concerns over incomplete or misdirected recovery. Such patterns are reminiscent of autoimmune predisposition and may be driven by antigen persistence, dysregulated cytokine networks, or failure in immune tolerance mechanisms. These findings highlight the need for longitudinal monitoring and immunomodulatory therapies in long COVID, particularly to prevent or manage autoimmune complications that may emerge during recovery.

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Hashtags:

#TCellImmunity, #LongCOVID, #COVID19Research, #SARSCoV2, #Immunology, #FlowCytometry, #TREC, #TCRFormation, #NaiveTCells, #EffectorMemory, #Th2Polarization, #Autoimmunity, #ImmuneDysregulation, #ThymicActivity, #CTLResponse, #ChronicInflammation, #ImmuneTolerance, #MolecularMimicry, #PostViralSyndrome, #TCellPhenotyping

Cancer, Inflammation & Immune Disorders Explored via Cellular Automaton 🧬 | #pencis #cancerresearch #immunology

INTRODUCTION: TUMOR-IMMUNE INTERACTIONS AND THE ROLE OF CELLULAR AUTOMATA

Cancer progression is a dynamic process shaped not only by genetic mutations but also by complex interactions with the immune system and inflammatory responses. The interplay between autoimmune activity and tumor behavior has become an area of intense research interest. This study presents a tumor-immune cellular automaton (CA) model designed to explore these relationships in depth. It simulates immune surveillance, immune evasion, and inflammation to evaluate how autoimmune disorders and immunosuppressive therapies affect tumor development. The CA model offers a computational perspective on personalized medicine by integrating immune status and tumor aggressiveness. By simulating comorbid cases where cancer coexists with immune dysfunction, the model contributes novel insights to guide future immunotherapy protocols and risk assessment strategies.

AUTOIMMUNITY AND TUMOR ACCELERATION: A DOUBLE-EDGED SWORD

Autoimmune diseases, traditionally seen as harmful due to their self-targeting immune responses, may paradoxically contribute to tumor progression in certain scenarios. Our CA simulations suggest that chronic inflammation and immune activation can foster a microenvironment conducive to tumor growth, especially when cancer cells exhibit high immune evasion capabilities. This interaction reflects clinical patterns where patients with autoimmune diseases exhibit increased cancer risk. Understanding the dual nature of autoimmune responses—both as potential tumor suppressors and accelerators—is critical in designing balanced therapeutic strategies.

IMMUNOSUPPRESSIVE THERAPIES AND THEIR ONCOLOGICAL IMPLICATIONS

Immunosuppressive treatments, though essential for managing autoimmune disorders and organ transplants, carry a well-documented risk of facilitating tumor growth. Our model supports this by demonstrating enhanced tumor proliferation in the presence of systemic immunosuppression. These findings emphasize the need to evaluate the long-term oncogenic potential of immune-modulating drugs and underline the importance of vigilant cancer screening protocols in immunosuppressed populations. The CA-based simulations help forecast risk scenarios and optimize immunosuppressive regimens to minimize cancer-related side effects.

IMMUNE EVASION IN CANCER: A CRUCIAL VARIABLE IN CA MODELING

One of the defining features of malignant tumors is their ability to evade immune detection and destruction. The cellular automaton model incorporates this phenomenon by assigning evasion parameters to tumor cells, influencing their survival in different immune landscapes. The simulations reveal that tumors with high immune evasion thrive even in hyperactive immune environments, suggesting the need for therapies targeting specific immune escape pathways. By adjusting evasion settings in the model, researchers can investigate the effectiveness of immune checkpoint inhibitors and design trials for resistant cancer types.

INFLAMMATION AS A CATALYST FOR TUMOR MICROENVIRONMENT CHANGES

Inflammatory responses play a pivotal role in shaping the tumor microenvironment. Chronic inflammation, as simulated in our CA model, alters immune cell behavior and nutrient flow, supporting tumor expansion and metastasis. This mirrors real-world data linking prolonged inflammation with cancer onset and progression. Targeting pro-inflammatory pathways could therefore offer preventive strategies in high-risk populations. The model’s ability to simulate varying degrees of inflammation provides a platform to test anti-inflammatory agents alongside conventional cancer therapies.

PERSONALIZED THERAPEUTICS: INSIGHTS FROM CELLULAR AUTOMATA

One of the most significant outcomes of this study is the potential for personalized medicine using computational modeling. By simulating individual profiles combining immune status, inflammation level, and tumor aggressiveness, the CA model serves as a predictive tool for treatment outcomes. It supports the idea that “one-size-fits-all” approaches are suboptimal in comorbid cases involving both immune dysfunction and cancer. Tailored immunotherapy protocols, dosage adjustments in immunosuppressants, and timing of interventions can all be refined using such computational frameworks.

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 Hashtags

#CancerResearch, #AutoimmuneDiseases, #TumorImmunology, #CellularAutomaton, #Inflammation, #ImmunosuppressiveTherapies, #ImmuneEvasion, #ComputationalBiology, #PersonalizedMedicine, #TumorMicroenvironment, #ImmuneModeling, #CancerSimulation, #ChronicInflammation, #Immunotherapy, #CancerRisk, #Comorbidities, #ClinicalOncology, #SystemsBiology, #CancerPrediction, #PencisConference,

Best Review Article Award 🏆 | Recognizing Scientific Excellence #ReviewAward #ScientificImpact #pencis

INTRODUCTION

The Best Review Article Award is a distinguished academic honor designed to recognize authors who demonstrate outstanding scholarly depth and critical insight through review articles. These reviews serve as the backbone of scientific synthesis, offering readers a consolidated understanding of current advancements, challenges, and future directions. Especially in dynamic disciplines such as infectious diseases, epidemiology, immunology, and public health, review articles help decode complex data and connect multiple research threads. With the support of Pencis Conferences, this award not only uplifts the contributions of top researchers but also encourages global knowledge exchange and collaboration. It is a celebration of those who shape science through analysis and interpretation.

CONTRIBUTION TO SCIENTIFIC COMMUNITY

Review articles are foundational to knowledge dissemination, providing critical overviews that help guide experimental design, policy development, and clinical strategies. This award recognizes the vital contribution researchers make by curating vast literature, comparing methodologies, and extracting meaningful patterns. Such articles act as educational tools for both emerging scholars and experts by organizing fragmented information into accessible formats. Honorees of the Best Review Article Award are seen as thought leaders whose work fuels academic growth and innovation within the global scientific ecosystem.

FOCUS ON INFECTIOUS DISEASES AND PUBLIC HEALTH

With growing global challenges such as pandemics, antimicrobial resistance, and emerging zoonotic threats, review articles in infectious diseases and public health are more critical than ever. These reviews highlight evolving trends, summarize vaccine breakthroughs, and map resistance patterns, offering policymakers and scientists an evidence-based compass. The Best Review Article Award emphasizes this strategic importance and honors those who produce high-impact reviews that support effective healthcare responses and scientific advancements worldwide.

INFLUENCE ON RESEARCH DIRECTIONS

The influence of a well-crafted review article goes beyond summarization—it redefines research priorities and inspires new hypotheses. Through meticulous synthesis, authors identify gaps in knowledge and recommend future paths for exploration. This award spotlights individuals whose work not only reflects current scientific consensus but also challenges the status quo to open new research frontiers. Winning this award solidifies an author's role as a navigator in the rapidly evolving scientific terrain.

ACADEMIC VISIBILITY AND COLLABORATION

Earning the Best Review Article Award dramatically enhances academic visibility and establishes a researcher’s authority in their field. Recognition by Pencis Conferences brings international exposure, opening doors to interdisciplinary collaborations, citations, and funding opportunities. This acknowledgment affirms the importance of intellectual synthesis and motivates others to contribute impactful reviews that shape scientific dialogue. It is more than an award—it is a platform for global engagement and academic leadership.

NOMINATION AND IMPACT

The nomination process for the Best Review Article Award is open to authors who have shown commitment to excellence in literature analysis and synthesis. Whether the review addresses vaccine innovations, epidemic modeling, or global health strategies, it must demonstrate clarity, depth, and scholarly impact. Winning this award amplifies the review’s influence in both academic and applied settings. It also highlights the nominee’s role as a communicator who bridges research communities and fosters knowledge progression.

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HASHTAGS

#BestReviewArticleAward, #ReviewArticleExcellence, #AcademicRecognition, #InfectiousDiseasesResearch, #ImmunologyReview, #PublicHealthInsights, #EpidemiologyReview, #ScientificLiterature, #GlobalHealthResearch, #PencisConferences, #ResearchSynthesis, #ScholarlyAward, #ScienceCommunication, #ReviewPaperImpact, #ScientificExcellence, #VaccineResearch, #AntimicrobialResistance, #EmergingInfections, #MedicalLiterature, #ResearchAwards,

Host-Pathogen Dynamics: Pseudomonas vs Nocardia in Yellow Croaker 🐟 | #pencis #pathogenstudy

 

INTRODUCTION

Nocardia seriolae and Pseudomonas plecoglossicida are emerging as major pathogens in aquaculture, especially due to their involvement in visceral granulomatous disease in economically significant fish species like the large yellow croaker (Larimichthys crocea). Despite their growing impact, the specific differences in host-pathogen interaction dynamics between these two bacterial species remain largely unexplored. In this study, comprehensive cellular and molecular analyses were conducted using a head kidney-derived cell line from yellow large croaker (LYC-hK) to investigate the distinct effects of both pathogens. Observations included ultrastructural changes via transmission electron microscopy, cellular damage through lactate dehydrogenase release, oxidative stress by ROS measurement, apoptosis, ferroptosis indicators, and comparative transcriptomic profiling. The integrated analysis helps elucidate bacterial strategies of immune evasion, cellular manipulation, and intracellular survival mechanisms. This study offers new perspectives into the pathogenesis of fish granulomatous infections and underscores the necessity for targeted therapeutics and immune interventions tailored to specific pathogens in aquaculture settings.

CELLULAR ULTRASTRUCTURE AND INTRACELLULAR PATHOGENESIS

The ultrastructural changes induced by N. seriolae and P. plecoglossicida in LYC-hK cells provide clear visual evidence of differing intracellular lifestyles. Transmission electron microscopy revealed that both bacteria successfully invaded and multiplied within host cells, yet they exhibited divergent morphological and structural modifications in the host cytoplasm. These disparities suggest variation in mechanisms of intracellular adaptation, evasion, or exploitation. The findings indicate that while both pathogens achieve intracellular persistence, their interactions with host cell organelles and membranes are distinct, possibly contributing to differences in pathogenic outcomes. Such insights into subcellular behavior are crucial for developing pathogen-specific interventions in fish immunology and aquaculture health management.

CYTOTOXICITY ASSESSMENT AND IMMUNE TOLERANCE

The cytotoxicity of P. plecoglossicida and N. seriolae toward host cells was evaluated through lactate dehydrogenase (LDH) release assays. Interestingly, both pathogens demonstrated low cytotoxicity levels in comparison to the highly cytotoxic Photobacterium damselae subsp. damselae. This suggests a strategy of immune evasion and intracellular persistence rather than acute cellular destruction. Low cytotoxic profiles may enable the bacteria to maintain a chronic presence, thereby facilitating granulomatous pathology without triggering immediate immune clearance. Understanding such mechanisms is critical to formulating vaccines or treatments that address chronic infections with low inflammatory signatures in aquaculture systems.

OXIDATIVE STRESS AND APOPTOSIS DIFFERENTIAL RESPONSES

One of the most striking findings was the difference in reactive oxygen species (ROS) generation and apoptosis induction between the two pathogens. N. seriolae triggered significantly higher levels of ROS and apoptosis compared to P. plecoglossicida, as detected by flow cytometry. This suggests that N. seriolae may elicit a stronger innate immune response or that it disrupts host oxidative balance more severely. These data imply that oxidative stress may be a pivotal mechanism in host response and disease progression. Targeting oxidative stress pathways could become a therapeutic strategy to control infections or limit tissue damage in affected fish species.

FERROPTOSIS AND IRON-HANDLING DYSREGULATION

The study explored ferroptosis-associated markers by measuring intracellular glutathione and iron levels. N. seriolae infection led to a notable depletion of intracellular GSH and GSH + GSSG, alongside an increase in Fe²⁺ levels—hallmarks of ferroptosis. This suggests that N. seriolae may induce cell death through oxidative iron-mediated pathways, which is distinct from the less pronounced impact of P. plecoglossicida. These findings point to the potential manipulation of host redox and iron metabolism as a strategy used by N. seriolae. Understanding ferroptosis in fish immunopathology opens doors for developing antioxidant or iron-modulating interventions in aquaculture.

TRANSCRIPTOMIC LANDSCAPE OF HOST RESPONSE

RNA-sequencing enabled a comparative view of the gene expression patterns induced by the two bacterial infections. The transcriptomic profiles revealed both overlapping and distinct regulatory networks involved in immune response, stress signaling, metabolism, and programmed cell death. While some pathways were universally activated by both pathogens, many genes were uniquely modulated depending on the infecting bacterium, highlighting pathogen-specific host interaction mechanisms. These findings lay the foundation for identifying potential biomarkers of infection, immune pathways for vaccine targeting, and transcriptional signatures predictive of disease severity in fish.

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 HASHTAGS:

#FishPathology, #AquacultureHealth, #NocardiaSeriolae, #PseudomonasPlecoglossicida, #HostPathogenInteraction, #FishImmunology, #VisceralGranulomatousDisease, #Transcriptomics, #OxidativeStress, #Ferroptosis, #ApoptosisPathways, #ElectronMicroscopy, #ROSProduction, #FishDiseaseResearch, #IntracellularBacteria, #CytotoxicityAssay, #RNASeqAnalysis, #YellowCroaker, #AquacultureResearch, #BacterialPathogenesis,

Impact of Socioeconomic Status on Long COVID & Quality of Life | French Nationwide Study 🇫🇷 #LongCOVID #Pencis

INTRODUCTION

The long-term consequences of SARS-CoV-2 infection, known as long COVID or post-COVID-19 condition (PCC), have emerged as a significant public health concern worldwide. In France, the burden of long COVID on individuals' quality of life remains a pressing issue, particularly as it interacts with socioeconomic disparities. This study investigates the role of socioeconomic position (SEP) in modulating the relationship between PCC and health-related quality of life (HRQoL) using data from a representative sample following the Omicron wave in autumn 2022. Through the application of the PROMIS-29 questionnaire across eight HRQoL domains, the research aims to uncover how variables like education, employment, income, and geographic origin shape the lived experience of long COVID in the French population. The study not only quantifies the impact but also explores equity and vulnerability across social strata, highlighting the importance of integrating socioeconomic perspectives into public health responses and healthcare strategies for long COVID.

RESEARCH DESIGN AND METHODOLOGY

The study utilized a cross-sectional, population-representative design to examine long COVID’s impact on HRQoL post-Omicron. A total of 1,448 adults previously infected with SARS-CoV-2 were surveyed, integrating robust demographic and socioeconomic data. The research adopted the WHO definition of PCC and employed the PROMIS-29 instrument to measure HRQoL across domains like physical function, pain, sleep, and mental well-being. A conceptual model was proposed to test how modifying factors—including age, sex, and SEP indicators such as education, employment status, and income—alter the relationship between PCC and HRQoL. Advanced statistical modeling techniques allowed for the analysis of interaction effects and helped identify subgroups with the most severe impacts.

SOCIOECONOMIC DETERMINANTS OF HRQOL

SEP was found to be a critical determinant in the relationship between long COVID and HRQoL. Individuals with low educational attainment and lower household income reported significantly greater reductions in quality of life across five PROMIS-29 domains, particularly in depression, fatigue, and social participation. These findings underscore the systemic vulnerability of marginalized groups to post-infection consequences. The study also revealed that business owners, unemployed individuals, and those from mainland France suffered disproportionately, suggesting economic instability and regional disparities amplify PCC-related health burdens. SEP is therefore not just a background factor but a core component influencing HRQoL outcomes post-COVID.

IMPACT OF EMPLOYMENT STATUS AND OCCUPATIONAL CATEGORY

Among the SEP indicators, employment status and occupational category were key modifiers. Unemployed individuals and self-employed persons, including entrepreneurs, exhibited greater HRQoL impairment than other employment groups. This pattern may be attributed to financial insecurity, lack of health coverage, and reduced social support. Moreover, these findings raise important considerations for labor policies and workplace support systems in mitigating the impact of long COVID, especially in populations whose income and well-being are tightly coupled to continuous employment or business viability.

REGIONAL AND EDUCATIONAL DISPARITIES

Geographic origin and educational attainment significantly modified the PCC-HRQoL relationship. Participants from mainland France experienced more pronounced declines in HRQoL, pointing to regional disparities that may stem from differences in healthcare access, economic infrastructure, or social services. Similarly, individuals without a long tertiary education were more vulnerable to PCC-related impairments. These disparities indicate that educational and regional policy interventions must be integrated into public health responses to long COVID to ensure equitable recovery and resilience across diverse French communities.

POLICY IMPLICATIONS AND RECOMMENDATIONS

The study’s findings carry significant implications for healthcare providers and policymakers. The evidence clearly supports the need for targeted interventions that consider SEP when addressing long COVID. Public health strategies must go beyond clinical treatment and incorporate social support, income assistance, and educational outreach to mitigate long COVID’s unequal burden. Special attention should be given to unemployed individuals, low-income families, and those with limited education. Policymakers are urged to design equity-focused programs to strengthen HRQoL recovery for all, especially for the socially and economically vulnerable populations.

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HASHTAGS:

#LongCOVID, #PostCOVID19Condition, #HealthInequality, #SocioeconomicStatus, #HRQoL, #PublicHealthFrance, #PROMIS29, #COVIDRecovery, #HealthEquity, #HealthPolicy, #OmicronWave, #FrenchHealthStudy, #SocialDeterminants, #HealthResearch, #ChronicCOVID, #IncomeAndHealth, #EmploymentHealthLink, #MentalHealthCOVID, #EducationAndHealth, #PCCImpact,

Declining Myocarditis Mortality in the U.S. 📉 | COVID-19 Pandemic Impact Explained #Myocarditis #Pencis

INTRODUCTION

Myocarditis, an inflammatory condition of the heart muscle, is associated with severe clinical complications including cardiogenic shock and life-threatening arrhythmias. In the United States, the trends of myocarditis-related mortality over the last two decades have been largely understudied, particularly in the context of the COVID-19 pandemic. This study aimed to analyze the temporal patterns, demographic disparities, and pandemic-era deviations in myocarditis mortality using national death records from 1999 to 2023, sourced from the CDC WONDER database. Through robust statistical methodologies including Joinpoint Regression and excess death calculations in R Studio, the study reveals a nuanced shift in mortality risk over time. A decline in deaths over two decades was disrupted dramatically during COVID-19, highlighting the need to understand infectious triggers and healthcare inequities. With nearly three-fourths of excess deaths in 2021 involving COVID-19, this investigation underscores the pandemic’s profound cardiovascular consequences and its intersection with existing health disparities across sex, race, and age.

LONG-TERM MORTALITY TRENDS FROM 1999 TO 2019

From 1999 to 2019, the age-adjusted mortality rate (AAMR) for myocarditis declined significantly in the United States. Beginning at 7.40 deaths per 1 million population in 1999, the rate dropped by 46.08% to 3.99 per million in 2019. This sustained decrease suggests improved healthcare access, earlier detection, or advancements in clinical management of myocarditis over time. The annual percentage change (APC) of −2.59 (95% CI: −2.97 to −2.24) reflects this favorable trend. However, this apparent progress masks underlying demographic disparities that persisted throughout the period. Despite the improvement, specific subpopulations—particularly older adults and Black Americans—continued to experience higher rates of mortality. These findings call attention to the need for targeted public health strategies and resource allocation to ensure that such declines in mortality are equitable and sustainable across all communities.

PANDEMIC-ERA SURGE IN MORTALITY: 2020–2021

The COVID-19 pandemic had a dramatic impact on myocarditis mortality trends, reversing two decades of progress within just two years. Between 2019 and 2021, the AAMR surged from 3.99 to 5.85, a 46.62% increase. The APC for this period reached 22.3%, a statistically significant and alarming shift. The year 2021 saw the highest excess mortality, with myocarditis deaths 54.94% above the expected number based on pre-pandemic trends. This reversal not only highlights the role of SARS-CoV-2 in triggering or exacerbating myocarditis but also indicates how strained healthcare systems and delayed care during the pandemic may have compounded outcomes. The sudden increase suggests an urgent need for both real-time surveillance systems and longitudinal research to track cardiac complications during infectious disease outbreaks.

COVID-19 AS A CONTRIBUTOR TO EXCESS MORTALITY

COVID-19 infection emerged as a dominant co-factor in excess myocarditis mortality during the pandemic. From 2020 to 2023, approximately 70.33% of the excess myocarditis-related deaths also involved confirmed COVID-19 infection. In 2021, this figure peaked at 76.15%, suggesting a strong association between SARS-CoV-2 and increased myocarditis risk or exacerbation of preexisting cardiac inflammation. The underlying mechanisms may include direct viral injury to myocardial cells, hyperinflammatory responses, and vascular involvement. These findings reinforce the broader impact of COVID-19 beyond respiratory illness, emphasizing its role in multisystem complications including cardiovascular mortality. It also points toward the necessity for COVID-related myocarditis awareness, early detection protocols, and post-infection cardiac monitoring, especially in vulnerable populations.

DISPARITIES IN DEMOGRAPHIC AND REGIONAL MORTALITY

The data from 1999 to 2023 consistently showed that myocarditis-related mortality was not equally distributed across demographics. Males, non-Hispanic Black or African Americans, and older adults bore a disproportionately higher burden. These disparities may stem from a combination of socioeconomic factors, comorbid conditions, healthcare access, and potential biological susceptibilities. The pandemic further magnified these inequities, with certain communities experiencing more profound surges in myocarditis deaths. This emphasizes the need for equity-driven health policy, focused interventions, and improved access to cardiac care in historically underserved regions and populations. Additionally, regional differences must be analyzed in future studies to determine if localized healthcare infrastructure or viral exposure played a role.

MORTALITY RECOVERY POST-PANDEMIC PEAK

Although the peak in myocarditis-related mortality occurred in 2021, subsequent years have shown partial recovery. By 2023, the AAMR had decreased to 4.33 per million, a reduction from the pandemic peak but still above the pre-pandemic baseline. This incomplete return to previous levels suggests a lasting impact of COVID-19 on cardiac health or residual healthcare disruptions. While it is encouraging that mortality rates declined after 2021, the persistent elevation underscores the importance of long-term follow-up for patients affected during the pandemic years. Continued monitoring and preventive strategies, including vaccination, post-acute COVID care, and cardiovascular rehabilitation, are vital in addressing the residual burden of myocarditis in the U.S. population.

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HASHTAGS

#Myocarditis, #CardiacHealth, #MortalityTrends, #COVID19Impact, #PandemicEffect, #PublicHealthResearch, #CardiovascularDisease, #HeartInflammation, #Epidemiology, #CDCWONDER, #JoinpointAnalysis, #ExcessDeaths, #HealthDisparities, #CardiacMortality, #HeartDiseaseStats, #COVID19Myocarditis, #RStudioAnalysis, #DemographicTrends, #HealthEquity, #USMortalityData,

Global Threat: Klebsiella Pneumoniae Carbapenemase (KPC) Resistance | #AMR #KPC #Pencis

🧬 INTRODUCTION

Klebsiella pneumoniae carbapenemases (KPCs) represent a significant class A β-lactamase threat among Gram-negative bacteria, known for their capacity to hydrolyze a wide spectrum of β-lactams, including carbapenems—the last line of defense in treating multidrug-resistant infections. This study aimed to systematically evaluate the global distribution, prevalence, and resistance patterns of KPC-producing Gram-negative bacterial clinical isolates. The comprehensive analysis incorporated data from 119 eligible studies out of 1993 screened articles, covering all major continents and offering a critical snapshot of KPC's global burden. These organisms, especially Klebsiella pneumoniae and Escherichia coli, are increasingly implicated in nosocomial outbreaks and community infections that pose severe challenges to current antibiotic therapies. This research reinforces the urgency to adopt robust infection control protocols and antimicrobial stewardship measures on a global scale.

🌍 GLOBAL DISTRIBUTION OF KPC-PRODUCING BACTERIA

The review confirms the widespread global distribution of KPC-producing Gram-negative pathogens. Asia led the published data with 49 studies, followed by Europe (29), North America (14), South America (11), and Africa (3), with an additional 13 studies spanning multiple continents. This wide geographic representation underscores the critical reality that no region remains unaffected. While high-income countries have reported relatively advanced detection and surveillance systems, resource-limited nations may underreport due to infrastructure constraints. The global reach of these resistant pathogens calls for international collaboration in monitoring and response mechanisms. Mobility of populations, international trade, and travel play key roles in the dissemination, making KPCs a true global public health concern.

🔬 MOLECULAR CHARACTERISTICS AND GENE DETECTION

The genetic characterization of the KPC-producing isolates revealed a dominant presence of the blaKPC-2 and blaKPC-3 genes. Among the studies evaluating specific genes, 91% (52/57) identified blaKPC-2, while 46% (26/57) reported blaKPC-3, indicating the prevalence of these two alleles in global outbreaks. These genes are frequently located on transferable plasmids, enhancing their potential for interspecies transmission. The molecular epidemiology of KPCs is essential not only for diagnostics but also for tracking the emergence of new resistant clones. Genetic surveillance helps in understanding local versus imported cases, guiding infection control policies. Continued research in this area is vital for early detection and tailored therapeutic strategies.

🦠 DIVERSITY OF BACTERIAL SPECIES PRODUCING KPC

While Klebsiella pneumoniae remains the most common KPC-producer (96 studies), the enzyme's presence in multiple other species indicates widespread gene dissemination. These include Escherichia coli (52), Enterobacter cloacae (31), Citrobacter spp. (24), and even less common isolates like Raoultella spp. and Morganella spp. This species diversity increases the difficulty of detection and treatment, especially in settings where laboratory capacity is limited. The evolution of resistance across a broad spectrum of clinically significant bacteria suggests that resistance is no longer restricted to a single genus but is rather an ecosystem-level threat. This highlights the need for comprehensive species-specific surveillance to understand the complete clinical burden.

💊 RESISTANCE PATTERNS AND TREATMENT CHALLENGES

One of the critical observations from the analysis is the resistance patterns displayed by these organisms. Resistance to ceftazidime–avibactam remained low (0–4%), making it one of the most promising therapeutic agents. However, resistance to other agents like polymyxins (4–80%), tigecycline (0–73%), and trimethoprim–sulfamethoxazole (5.6–100%) was more variable and often alarmingly high. These findings reflect the shrinking therapeutic arsenal available to clinicians. Multidrug resistance among KPC-producing isolates not only leads to limited treatment options but also results in increased morbidity, mortality, and healthcare costs. These resistance patterns call for the urgent development of novel antimicrobials and optimization of combination therapies.

🛡️ RECOMMENDATIONS AND GLOBAL IMPLICATIONS

The global rise in KPC-producing organisms necessitates immediate action through comprehensive infection control strategies, including enhanced hand hygiene, contact precautions, and environmental decontamination. Equally important are antimicrobial stewardship programs to curb the inappropriate use of broad-spectrum antibiotics. International guidelines should support the integration of rapid molecular diagnostic tools and real-time data-sharing platforms. Given the ability of KPC genes to rapidly disseminate across species and continents, a “One Health” approach that encompasses human, animal, and environmental health is required. Without a coordinated global response, KPCs could escalate into an untreatable pandemic-level antimicrobial resistance crisis.

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HASHTAGS

#KPCResistance, #AntimicrobialResistance, #KlebsiellaPneumoniae, #BetaLactamase, #GlobalHealthThreat, #GramNegativeBacteria, #SuperbugCrisis, #DrugResistance, #CarbapenemResistance, #InfectionControl, #GlobalEpidemiology, #MultidrugResistance, #HospitalInfections, #MolecularDiagnostics, #AntibioticStewardship, #ResistanceSurveillance, #blaKPC2, #blaKPC3, #NovelAntibiotics, #MicrobialGenomics,

Antimicrobial Resistance in Diabetic UTI Patients | Tanzania Study 🔬 #AMR #UTI #Diabetes #Pencis

INTRODUCTION

Urinary tract infections (UTIs) are among the most common bacterial infections globally and are a significant source of morbidity, particularly among vulnerable populations such as diabetic patients. Individuals with diabetes mellitus (DM) are more prone to infections due to compromised immune responses, making them especially susceptible to genitourinary tract infections. This vulnerability, combined with the frequent use of antibiotics to manage UTIs, has contributed to the growing public health crisis of antimicrobial resistance (AMR). In Tanzania, particularly at Benjamin Mkapa Hospital in Dodoma, the burden of antimicrobial resistance is intensifying among diabetic patients diagnosed with UTIs. The indiscriminate use of antibiotics—often without prescriptions—has exacerbated the emergence of resistant uropathogens, leading to prolonged hospital stays, increased healthcare costs, and treatment failures. This research was conducted to assess the prevalence and associated factors of AMR in diabetic UTI patients and to identify actionable insights for clinical management and public health interventions.

METHODOLOGICAL INSIGHTS

This research adopted a hospital-based cross-sectional study design, enrolling 419 diabetic patients aged 30 years and above diagnosed with urinary tract infections at Benjamin Mkapa Hospital. The methodological rigor was maintained by collecting a single urine specimen from each patient and culturing it on both Cystine Lactose Electrolyte Deficient (CLED) and blood agar media. The purpose was to isolate and identify the causative organisms responsible for UTIs in this population. Data were statistically analyzed using SPSS version 20, with descriptive analysis expressed in proportions and percentages. The study further utilized logistic regression to determine associations between antimicrobial resistance and sociodemographic factors. This methodological approach ensured both the reliability and relevance of the findings in terms of identifying key behavioral and socioeconomic contributors to antibiotic resistance among diabetic UTI patients.

SOCIOECONOMIC DETERMINANTS OF AMR

The study found a strong statistical association between antimicrobial resistance and several socioeconomic factors, including employment status and income level. Working participants exhibited higher levels of antimicrobial resistance (P = 0.000), and similarly, jobless participants were also significantly associated with AMR (P = 0.000), indicating a complex socio-behavioral dynamic. Low-income individuals (P = 0.046) also showed increased prevalence of resistant infections. These findings underscore the need to consider socioeconomic determinants when designing interventions to combat AMR. Awareness campaigns and healthcare strategies must be tailored not only to medical needs but also to economic realities, which often influence access to healthcare, drug affordability, and adherence to prescription guidelines.

MISUSE OF ANTIBIOTICS AND PUBLIC BEHAVIOR

One of the most alarming revelations of this study was the high correlation between antibiotic misuse and the rise of antimicrobial resistance. A significant number of patients admitted to using antibiotics without prescriptions (P = 0.001), which directly contributed to the proliferation of resistant uropathogens. This highlights a critical gap in public health literacy regarding the proper use of antibiotics. Over-the-counter access, lack of regulation, and limited awareness have created an environment where antibiotics are used as quick fixes, often without any microbiological confirmation. Public education, stricter prescription enforcement, and community engagement programs are essential to curb irrational drug use and reduce AMR risks.

MICROBIOLOGICAL CULTURE OUTCOMES

The culturing of urine samples on CLED and blood agar media yielded crucial data on the types of pathogens involved and their resistance patterns. The microbiological analysis served as the backbone of this research, providing empirical evidence to support the prevalence of resistant strains among diabetic UTI patients. These findings are not only important for local treatment guidelines but also contribute to the larger body of global AMR surveillance. Periodic bacterial culture testing and sensitivity profiling should be institutionalized in hospital settings, especially for high-risk groups like diabetics, to ensure targeted antibiotic therapy and minimize empirical treatment that fuels resistance.

RECOMMENDATIONS AND PUBLIC HEALTH IMPLICATIONS

This study concludes with a call to action for healthcare policymakers, practitioners, and community leaders. The results clearly point toward the urgent need for regular epidemiological surveillance of antibiotic resistance patterns, particularly among diabetic patients with UTIs. Public education campaigns must be intensified to promote the responsible use of antibiotics. Hospital infection control protocols should include routine culture and sensitivity testing to guide appropriate treatment. Additionally, socioeconomic disparities must be addressed through policy interventions that improve healthcare access and patient education. Only a coordinated, multidisciplinary approach can effectively halt the growing threat of antimicrobial resistance in vulnerable populations.

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Hashtags

#UTIResearch, #AntimicrobialResistance, #DiabeticHealth, #UTITanzania, #BenjaminMkapaHospital, #DodomaHealthStudy, #InfectiousDiseases, #PublicHealthCrisis, #BacterialResistance, #DiabetesAndInfection, #HospitalBasedStudy, #CrossSectionalResearch, #AMRinUTI, #AntibioticMisuse, #ResistanceSurveillance, #MicrobialResistance, #Uropathogens, #CLEDMedia, #HealthPolicy, #GlobalHealthChallenge

🏆 Best Paper Award 2025 | Recognizing Excellence in Research | #Pencis #BestPaperAward #InfectiousDiseases

🏆 INTRODUCTION

The Best Paper Award 2025, a premier recognition presented by Pencis Conferences, shines a spotlight on pioneering scientific work that has the power to transform the field of infectious diseases and public health. Celebrated as one of the most prestigious honors in the academic world, this award is more than a trophy—it's a testament to innovation, rigor, and real-world impact. With a meticulous peer-review process guided by top-tier scholars and research leaders, the award validates research excellence while nurturing a global scientific community. Winners are invited to present their groundbreaking findings at the International Conference on Infectious Diseases, fostering deeper conversations and international collaboration. The award serves as both a motivator and a milestone, inspiring researchers—whether emerging talents or seasoned professionals—to push the boundaries of what science can achieve. As global health threats continue to challenge our systems, recognizing and rewarding innovation in this space has never been more critical.

🔬 RESEARCH EXCELLENCE IN INFECTIOUS DISEASES

Scientific research in infectious diseases is a cornerstone of global health resilience, and the Best Paper Award 2025 places a sharp focus on elevating this critical domain. The award is a tribute to original work that not only deepens our understanding of pathogen biology and epidemiology but also brings new therapeutic and preventive strategies to light. Whether it's combatting viral outbreaks, drug-resistant bacteria, or neglected tropical diseases, the selected research represents cutting-edge science at its finest. The awardees are pioneers whose work has the potential to influence policy, drive medical innovation, and protect communities around the world. Their contributions reflect a commitment to excellence, backed by data-driven insights and novel methodologies that redefine public health outcomes.

🌍 GLOBAL IMPACT AND PUBLIC HEALTH SIGNIFICANCE

One of the defining attributes of the Best Paper Award is its emphasis on research that generates a measurable global impact. In a world where emerging infectious diseases threaten societal stability, the award highlights studies that not only advance scientific knowledge but also provide solutions with direct application to public health systems. From improving vaccine efficacy to developing rapid diagnostics and effective surveillance methods, winning papers serve as blueprints for tackling real-world challenges. The award inspires researchers to transcend academic boundaries and engage with the pressing health issues faced by underserved populations globally. Through these efforts, science becomes a driving force for health equity and international preparedness.

🧠 INNOVATION THROUGH INTERDISCIPLINARY RESEARCH

Innovation thrives at the crossroads of disciplines, and the Best Paper Award 2025 recognizes this by honoring research that integrates fields such as microbiology, data science, epidemiology, and immunology. Award-winning papers often embody a fusion of ideas, using technological advancements like AI, genomics, or bioinformatics to solve complex health problems. This interdisciplinary approach not only boosts research relevance but also accelerates the translation of findings into actionable health strategies. By rewarding boundary-breaking work, the award encourages a spirit of intellectual curiosity and collaboration across disciplines, nurturing a generation of researchers equipped to tackle evolving public health threats.

🤝 COLLABORATION AND NETWORKING OPPORTUNITIES

Winning the Best Paper Award is not only a personal achievement but also a gateway to global collaboration. Awardees are invited to present their work at the Pencis-hosted International Conference on Infectious Diseases, where they can engage with researchers, clinicians, policymakers, and funders from around the world. These interactions pave the way for cross-border projects, knowledge exchange, and long-term scientific partnerships. The award strengthens the global research network by connecting like-minded experts dedicated to a common goal—defending humanity against infectious disease threats. This collaborative ecosystem is critical for advancing research and accelerating innovations that benefit all.

🎓 ENCOURAGING EARLY-CAREER RESEARCHERS

The Best Paper Award 2025 is designed not only to honor established scholars but also to empower emerging voices in science. By providing early-career researchers with recognition, visibility, and a platform to showcase their work, the award helps shape the future of public health research. Young investigators are encouraged to submit high-quality, original studies, knowing that their efforts will be reviewed and appreciated by leaders in the field. This inclusive approach builds confidence, fosters mentorship, and supports the next wave of innovators who will carry forward the mission of combating infectious diseases worldwide.

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#BestPaperAward2025, #PencisConferences, #InfectiousDiseases, #PublicHealthResearch, #GlobalHealth, #ScientificExcellence, #ResearchRecognition, #AcademicAwards, #HealthInnovation, #PeerReview, #ResearchLeadership, #MedicalScience, #GlobalCollaboration, #DiseasePrevention, #ScientificBreakthroughs, #AwardWinningResearch, #ConferencePresentation, #PublicHealthImpact, #ScientificAchievement, #PencisAwards

Deep Learning Unveils Liver Metastasis Risks in Pancreatic Cancer | Genomic AI Model 🔬 #PancreaticCancer #AIModel #LiverMetastasis #pencis

                                                       

INTRODUCTION

Pancreatic ductal adenocarcinoma (PDAC) is notorious for its poor prognosis and high rate of occult metastasis at initial diagnosis, significantly limiting the benefit of local surgical treatment. Despite advances in imaging and staging, many patients with resectable disease eventually show early systemic relapse, indicating a need for improved risk stratification methods. The integration of genomic insights with artificial intelligence models has the potential to reshape how we classify tumor biology preoperatively. This study introduces a novel deep learning framework, PanScore, which combines eight critical genomic features to predict liver metastasis, the most lethal dissemination site in PDAC. By stratifying patients based on survival risk, PanScore moves beyond conventional radiological and pathological staging. This research paves the way for applying precision oncology in pancreatic cancer, offering a new avenue for optimizing surgical decisions and systemic therapy planning.

GENOMIC FEATURE SELECTION FOR RISK STRATIFICATION

The foundation of PanScore’s predictive power lies in its careful selection of genomic biomarkers associated with metastatic potential in PDAC. Through retrospective analysis of the MSK-MET dataset, the study pinpointed eight key genomic alterations with strong statistical correlation to liver metastasis: tumor mutational burden (TMB), fraction genome altered (FGA), TP53 mutation status, and copy number variations in AKT2, MYC, KRAS, CDKN2A, and SMAD4. These features not only meet the prevalence threshold of 2.5% but also exhibit significant p-values (<0.05), underscoring their clinical relevance. This precise feature curation forms a crucial step in ensuring model interpretability and performance, demonstrating the importance of biologically informed input selection in deep learning for oncology.

MODEL DEVELOPMENT AND ARCHITECTURE OPTIMIZATION

The PanScore model was developed using the H2O AutoML platform, which facilitates rapid construction and evaluation of various machine learning models. Using five-fold cross-validation and AUC-based ranking, the most accurate model was fine-tuned into a six-layer deep neural network. Hyperparameter optimization ensured the model's robustness across training datasets. The use of automated machine learning workflows allowed systematic evaluation of different architectures and learning parameters, improving generalizability. By leveraging this approach, the study ensured that PanScore maintains high predictive accuracy while remaining adaptable to future datasets and potentially scalable for clinical applications.

SURVIVAL STRATIFICATION AND PROGNOSTIC VALIDATION

A key achievement of PanScore is its ability to stratify patients into three clinically distinct survival risk categories: low, intermediate, and high. Within the MSK-MET cohort, these risk groups demonstrated significantly different median overall survival times (21.39, 15.34, and 9.36 months, respectively; p < 0.001). The hazard ratio for high vs. low-risk groups was 2.07, indicating a strong prognostic signal. Independent validation using the MSK-IMPACT dataset (n=2181) further confirmed these distinctions. For patients with radiographically resectable PDAC, PanScore identified subgroups with survival ranging from 35.4 to 17.9 months—information not captured by conventional staging. This robust validation highlights PanScore’s potential as a reliable biomarker-driven tool in clinical oncology.

IMPLICATIONS FOR RESECTABLE PDAC AND OCCULT METASTASIS

One of PanScore’s most impactful findings is its capacity to uncover biologically aggressive PDAC phenotypes among radiographically resectable cases. In the MSK-IMPACT validation cohort, patients deemed resectable yet assigned a high PanScore exhibited survival outcomes similar to those with borderline or locally advanced disease. This suggests that PanScore may detect molecular signatures of occult metastasis not visible on imaging. Such insights can inform preoperative decision-making, potentially guiding neoadjuvant therapy or delaying surgery in favor of systemic treatment. This has profound implications for improving the precision of PDAC management and reducing early post-surgical relapse.

FUTURE DIRECTIONS AND CLINICAL INTEGRATION

The integration of PanScore into clinical workflows may herald a new era in PDAC care, where treatment planning is informed not only by anatomical staging but also by underlying tumor biology. Future research should focus on prospective validation across multi-institutional cohorts, testing its predictive value in real-time clinical settings. Additionally, integrating PanScore with imaging, histopathology, and other omics data could enhance its predictive capability. As deep learning tools become increasingly accepted in oncology, regulatory approval, interpretability, and clinician education will be key for adoption. Ultimately, PanScore holds promise for enabling a more personalized and evidence-based approach to managing pancreatic cancer.

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HASHTAGS

#PanScore, #PDAC, #PancreaticCancer, #DeepLearning, #GenomicMedicine, #AIinOncology, #CancerGenomics, #LiverMetastasis, #PrecisionOncology, #MachineLearning, #SurvivalPrediction, #Bioinformatics, #MSKIMPACT, #MSKMET, #TumorBiomarkers, #TP53, #KRAS, #CDKN2A, #SMAD4, #H2OAutoML,