Search Results (1,046)

Background: The COVID-19 pandemic has profoundly altered the clinical and microbiological landscape of respiratory tract infections (RTIs), potentially reshaping pathogen distribution and antimicrobial resistance (AMR) profiles across care settings. Objectives: The objective of this study was to assess temporal trends in respiratory bacterial pathogens, antimicrobial resistance, and polymicrobial infections across three pandemic phases—pre-COVID (2018–2019), COVID (2020–2022), and post-COVID (2022–2024)—in hospitalized and ambulatory patients. Methods: We retrospectively analyzed 1827 respiratory bacterial isolates (hospitalized patients, n = 1032; ambulatory patients, n = 795) collected at a tertiary care center in Northern Italy. Data were stratified by care setting, anatomical site, and pandemic phase. Species identification and susceptibility testing followed EUCAST guidelines. Statistical analysis included chi-square and Fisher’s exact tests. Results: In hospitalized patients, a significant increase in Pseudomonas aeruginosa (from 45.5% pre-COVID to 58.6% post-COVID, p < 0.0001) and Acinetobacter baumannii (from 1.2% to 11.1% during COVID, p < 0.0001) was observed, with 100% extensively drug-resistant (XDR) rates for A. baumannii during the pandemic. Conversely, Staphylococcus aureus significantly declined from 23.6% pre-COVID to 13.7% post-COVID (p = 0.0012). In ambulatory patients, polymicrobial infections peaked at 41.2% during COVID, frequently involving co-isolation of Candida spp. Notably, resistance to benzylpenicillin in Streptococcus pneumoniae reached 80% (4/5 isolates) in hospitalized patients during COVID, and carbapenem-resistant P. aeruginosa (CRPA) significantly increased post-pandemic in ambulatory patients (0% pre-COVID vs. 23.5% post-COVID, p = 0.0014). Conclusions: The pandemic markedly shifted respiratory pathogen dynamics and resistance profiles, with distinct trends observed in hospital and community settings. Persistent resistance phenotypes and frequent polymicrobial infections, particularly involving Candida spp. in outpatients, underscore the need for targeted surveillance and antimicrobial stewardship strategies. Full article

Background/Objectives: Phase II oncology trials often rely on single-arm designs to test $H_0:\pi ={\pi }_0$ versus $H_a:\pi >{\pi }_0$, especially when randomized trials are infeasible due to cost or disease rarity. Traditional approaches, such as the exact binomial test and Simon’s two-stage design, tend to be conservative, with actual Type I error rates falling below the nominal $\alpha$ due to the discreteness of the underlying binomial distribution. This study aims to develop a more efficient and flexible method that maintains accurate Type I error control in such settings. Methods: We propose a convolution-based method that combines the binomial distribution with a simulated normal variable to construct an unbiased estimator of $\pi$. This method is designed to precisely control the Type I error rate while enabling more efficient trial designs. We derive its theoretical properties and assess its performance against traditional exact tests in both one-stage and two-stage trial designs. Results: The proposed method results in more efficient designs with reduced sample sizes compared to standard approaches, without compromising the control of Type I error rates. We introduce a new two-stage design incorporating interim futility analysis and compare it with Simon’s design. Simulations and real-world examples demonstrate that the proposed approach can significantly lower trial cost and duration. Conclusions: This convolution-based approach offers a flexible and efficient alternative to traditional methods for early-phase oncology trial design. It addresses the conservativeness of existing designs and provides practical benefits in terms of resource use and study timelines. Full article

Brain organoid technology has revolutionized in vitro modeling of human neurodevelopment and disease, providing unprecedented insights into cortical patterning, neural circuit assembly, and pathogenic mechanisms of neurological disorders. Critically, human brain organoids uniquely recapitulate human-specific developmental processes—such as the expansion of outer radial glia and neuromelanin—that are absent in rodent models, making them indispensable for studying human brain evolution and dysfunction. However, a major bottleneck persists: Extended culture periods (≥6 months) are empirically required to achieve late-stage maturation markers like synaptic refinement, functional network plasticity, and gliogenesis. Yet prolonged conventional 3D culture exacerbates metabolic stress, hypoxia-induced necrosis, and microenvironmental instability, leading to asynchronous tissue maturation—electrophysiologically active superficial layers juxtaposed with degenerating cores. This immaturity/heterogeneity severely limits their utility in modeling adult-onset disorders (e.g., Alzheimer’s disease) and high-fidelity drug screening, as organoids fail to recapitulate postnatal transcriptional signatures or neurovascular interactions without bioengineering interventions. We summarize emerging strategies to decouple maturation milestones from rigid temporal frameworks, emphasizing the synergistic integration of chronological optimization (e.g., vascularized co-cultures) and active bioengineering accelerators (e.g., electrical stimulation and microfluidics). By bridging biological timelines with scalable engineering, this review charts a roadmap to generate translationally relevant, functionally mature brain organoids. Full article

Background/Objectives: Oral cancer resection often leads to maxillofacial defects and dentition loss, compromising patients’ quality of life. Implant-supported prosthetic rehabilitation offers a reliable solution to restore function, though factors such as bone reconstruction, radiotherapy, and timing of implant placement (immediate vs. delayed) may influence outcomes. This study aimed to evaluate long-term implant survival and rehabilitation timelines in oncologic patients, comparing two cohorts (2005–2014 and 2015–2024) to assess the impact of evolving clinical practices. Methods: A retrospective cohort study was conducted at Hospital General Universitario Gregorio Marañón (Madrid, Spain), including 304 patients who underwent ablative oral cancer surgery and subsequent implant-based rehabilitation between 2005 and 2024. Data on demographics, oncologic treatment, reconstruction, implant timing, and prosthetic rehabilitation were collected. Outcomes were compared using Kaplan–Meier analysis and appropriate statistical tests between the 2005–2014 (n = 122) and 2015–2024 (n = 182) cohorts. Results: A total of 2341 Ticare Implants^® were placed, supporting 281 prostheses. Implant placement during primary surgery increased from 41% to 71% (p < 0.001). The median time from surgery to prosthesis significantly decreased from 24 to 15 months (p < 0.001). Five-year implant survival was 95% in the early cohort versus 97% in the later cohort. Implant survival was comparable between irradiated and non-irradiated patients (~94–96%). Fixed prostheses became more frequent (92% vs. 79%, p = 0.002). Conclusions: Implant-supported rehabilitation in oncologic patients is highly feasible and durable, with improved timelines and functional outcomes associated with early implant placement and modern digital planning strategies. Full article

Background: We recently reported sex-dependent impairment in cognitive functions in male and female mice exposed for 24 h, 48 h or 15 days to 2G hypergravity (HG). Methods: In the present study, we investigated brain functional correlates by analyzing synaptic activity and plasticity in the CA1 area of the hippocampus in both genders of mice previously exposed to 2G for the same duration. This was assessed by electrophysiological extracellular recordings in ex vivo slice preparations. Results: Basal synaptic transmission and glutamate release were unchanged regardless of HG duration. However, plasticity was altered in a sex- and time-specific manner. In males, long-term potentiation (LTP) induced by strong high-frequency stimulation and NMDA receptor (NMDAr) activation was reduced by 26% after 24 h of exposure but recovered at later timepoints. This deficit was reversed by D-serine or glycine, suggesting decreased activation at the NMDAr co-agonist site. In females, LTP deficits (23%) were found only after 15 days following mild theta burst stimulation and were not reversed by D-serine. Long-term depression (LTD) was unaffected in both sexes. Conclusions: This study highlights, for the first time, sex-dependent divergence in the CA1 hippocampal plasticity timeline following 2G exposure. The synaptic changes depend on exposure duration and the stimulation protocol and could underlie the previously observed cognitive deficits. Full article

The convergence of artificial intelligence (AI) and nanomedicine has transformed cancer vaccine development, particularly in optimizing RNA-loaded lipid nanoparticles (LNPs). Stability and targeted delivery are major obstacles to the clinical translation of promising RNA-LNP vaccines for cancer immunotherapy. This systematic review analyzes the AI’s impact on LNP engineering through machine learning-driven predictive models, generative adversarial networks (GANs) for novel lipid design, and neural network-enhanced biodistribution prediction. AI reduces the therapeutic development timeline through accelerated virtual screening of millions of lipid combinations, compared to conventional high-throughput screening. Furthermore, AI-optimized LNPs demonstrate improved tumor targeting. GAN-generated lipids show structural novelty while maintaining higher encapsulation efficiency; graph neural networks predict RNA-LNP binding affinity with high accuracy vs. experimental data; digital twins reduce lyophilization optimization from years to months; and federated learning models enable multi-institutional data sharing. We propose a framework to address key technical challenges: training data quality (min. 15,000 lipid structures), model interpretability (SHAP > 0.65), and regulatory compliance (21CFR Part 11). AI integration reduces manufacturing costs and makes personalized cancer vaccine affordable. Future directions need to prioritize quantum machine learning for stability prediction and edge computing for real-time formulation modifications. Full article

Background: COVID-19-associated coagulopathy (CAC) is a complex condition, with high rates of thrombosis, high levels of inflammation markers and hypercoagulation (increased levels of fibrinogen and D-Dimer), as well as extensive microthrombosis in the lungs and other organs of the deceased. It resembles, without being identical, other coagulation disorders such as sepsis-DIC (SIC/DIC), hemophagocyte syndrome (HPS) and thrombotic microangiopathy (TMA). Platelets (PLTs), key regulators of thrombosis, inflammation and immunity, are considered an important risk mediator in COVID-19 pathogenesis. Platelet index MPV/PLT ratio is reported in the literature as more specific in the prognosis of platelet-related systemic thrombogenicity. Studies of MPV/PLT ratio with regards to the severity of COVID-19 disease are limited, and there are no references regarding this ratio to the outcome of COVID-19 disease at specific time points of hospitalization. The aim of this study is to evaluate the relationship of COVID-19 mortality with the red cell distribution width–coefficient of variation (RDW-CV), platelets and MPV/PLT ratio parameters. Methods: Values of these parameters in 511 COVID-19 hospitalized patients were recorded (a) on admission, (b) as mean values of the 1st and 2nd week of hospitalization, (c) over the total duration of hospitalization, (d) as nadir and zenith values, and (e) at discharge. Results: As for mortality (survivors vs. deceased), statistical analysis with ROC curves showed that regarding the values of the parameters on admission, only the RDW-CV baseline was of prognostic value. Platelet parameters, absolute number and MPV/PLT ratio had predictive potential for the disease outcome only as 2nd week values. On the contrary, with regards to disease severity (mild/moderate versus severe/critical), only the MPV/PLT ratio on admission can be used for prognosis, and to a moderate degree. On multivariable logistic regression analysis, only the RDW-CV mean hospitalization value (RDW-CV mean) was an independent and prognostic variable for mortality. Regarding disease severity, the MPV/PLT ratio on admission and RDW-CV mean were independent and prognostic variables. Conclusions: RDW-CV, platelets and MPV/PLT ratio hematological parameters could be of predictive value for mortality and severity in COVID-19 disease, depending on the hospitalization timeline. Full article

This paper proposes a strategic framework to enhance nuclear energy governance by advancing the Qudrat-Ullah Nuclear Peace and Trust (Q-NPT) framework. Designed to complement existing treaties such as the Nuclear Non-Proliferation Treaty (NPT) and International Atomic Energy Agency (IAEA) safeguards, Q-NPT integrates principles of equity, transparency, and trust to address persistent governance challenges. The framework emphasizes sustainable nuclear technology access, multilateral cooperation, and integration with global energy transition goals. Through an analysis of institutional, economic, technological, and geopolitical barriers, the study outlines actionable pathways for adoption, including legal harmonization, differentiated financial instruments, and deployment of advanced verification technologies such as blockchain, artificial intelligence (AI), and remote monitoring. A phased implementation timeline is presented, enabling adaptive learning and stakeholder engagement over short-, medium-, and long-term horizons. Regional case studies, including Serbia and Latin America, demonstrate the framework’s applicability in diverse contexts. By linking nuclear policy to broader climate, energy equity, and global security objectives, Q-NPT offers an operational and inclusive roadmap for future-ready governance. This approach contributes to the literature on energy systems transformation by situating nuclear governance within a sustainability-oriented, trust-centered paradigm. Full article

Background/Objectives: In late 2019, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) caused a pandemic called the ‘coronavirus disease 2019’ (COVID-19). After the acute SARS-CoV-2 infection, many individuals (up to 33%) complained of unexplained symptoms involving multiple organ systems and were diagnosed as having Long COVID-19 (LC-19). Currently, LC-19 is inadequately defined, requiring the formation of consistent diagnostic parameters to provide a foundation for ongoing and future studies of epidemiology, risk factors, clinical characteristics, and therapy. LC-19 represents a significant burden on multiple levels. The reduced ability of workers to return to work or compromised work efficiency has led to consequences at national, economic, and societal levels by increasing dependence on community services. On a personal scale, the isolation and helplessness caused by the disease and its subsequent impact on the patient’s mental health and quality of life are incalculable. Methods: In this paper, we used Walker and Avants’ eight-step approach to perform a concept analysis of the term “Long COVID-19” and define its impact across these parameters. Results: Using this methodology, we provide an improved definition of LC-19 by connecting the clinical symptomology with previously under-addressed factors, such as mental, psychological, economic, and social effects. This definition of LC-19 features can help improve diagnostic procedures and help plan relevant healthcare services. Conclusions: LC-19 represents a complex and pressing public health challenge with diverse symptomology, an unpredictable timeline, and complex pathophysiology. This concept analysis serves as a tool for improving LC-19 definition, but it remains a dynamic disease with evolving diagnostic and therapeutic approaches, requiring deeper investigation and understanding of its long-term effects. Full article

This study investigates the long-term variability and extremes of monthly precipitation during 150 years or more at 4 locations in Argentina: Corrientes, Córdoba, Buenos Aires, and Bahía Blanca. Annual and seasonal trends, extreme dry and wet months over the whole period, and the relationships between large-scale climate drivers and monthly rainfall are considered. Results show that, except for Córdoba, the complete anomaly timeseries trend analysis for all other stations yielded null trends over the centennial study period. Considerable month-to-month variability is observed for all locations together with the existence of low-frequency decadal to interdecadal variability, both for monthly precipitation anomalies and for statistically significant excess and deficit months. Linear fits considering oceanic climate indicators as drivers of variability yield significant differences between locations, while not between full records and seasonally sampled. Issues regarding the use of linear analysis to quantify variability, the dispersion along the timeline of record extreme rainy months at each location, together with the evidence of severe daily precipitation events not necessarily coinciding with the ranking of the rainiest months at each location, highlights the challenges of understanding the drivers of variability of both monthly and severe daily precipitation and the need of using extended centennial timeseries whenever possible. Full article

Chronic inflammatory response syndrome (CIRS) and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) are debilitating multisystem illnesses that share overlapping symptoms and molecular patterns, including immune dysregulation, mitochondrial impairment, and vascular dysfunction. This review provides a chronological synthesis of biomarker development in CIRS, tracing its evolution from early functional tests such as visual contrast sensitivity (VCS) to advanced transcriptomic profiling. Drawing on peer-reviewed studies spanning two decades, we examine the layered integration of neuroendocrine, immunologic, metabolic, and genomic markers that collectively support a multisystem model of innate immune activation specific to environmentally acquired illness. Particular focus is given to the Gene Expression: Inflammation Explained (GENIE) platform’s use of transcriptomics to classify disease stages and distinguish CIRS from other fatiguing conditions. While ME/CFS research continues to explore overlapping pathophysiologic features, it has yet to establish a unified diagnostic model with validated biomarkers or exposure-linked mechanisms. As a result, many patients labeled with ME/CFS may, in fact, represent unrecognized CIRS cases. This review underscores the importance of structured biomarker timelines in improving differential diagnosis and guiding treatment in complex chronic illness and highlights the reproducibility of the CIRS framework in contrast to the diagnostic ambiguity surrounding ME/CFS. Full article

Background/Objectives: An initial COVID-19 candidate vaccine containing a purified ancestral SARS-CoV-2 spike antigen was characterized with an ELISA using recombinant monoclonal antibodies (mAbs) generated against this variant. Upon the emergence of a new Beta (B.1.351) spike variant early in the pandemic, the assessment of a bivalent vaccine containing ancestral and Beta spike antigens began. Due to accelerated project timelines, mAbs generated specifically against the Beta spike antigen were not available at the time to address assay development and vaccine testing requirements. Methods: Using only the initial mAb panel raised against the ancestral spike antigen, an epitope-blocking ELISA strategy was developed to independently measure Beta spike antigen in bivalent vaccine formulations. To facilitate this, epitope profiling of spike antigens from both ancestral and Beta variants was performed with biolayer interferometry and hydrogen–deuterium exchange mass spectrometry using the original panel of mAbs. Results: The resulting blocking ELISA was precise and specific for the Beta spike antigen and detected the expected amount of this antigen in bivalent vaccine formulations. The specific amount of ancestral spike protein in the bivalent vaccine was also confirmed using the original ELISA developed at the onset of the pandemic. Conclusions: This epitope-blocking strategy helped to overcome key reagent availability issues and could be applied to other projects involving related proteins. Full article

Background: Brazilian Jiu-Jitsu (BJJ) is a physically demanding sport associated with a notable risk of musculoskeletal injuries. Understanding injury patterns, rehabilitation approaches, and psychological readiness to return to sport (RTS) is essential for prevention and management strategies. This study aimed to investigate injury characteristics among Italian BJJ athletes, assess their rehabilitation processes and psychological recovery, and identify key risk factors such as belt level, body mass index (BMI), and training load. Methods: A cross-sectional survey was conducted among members of the Italian BJJ community, including amateur and competitive athletes. A total of 360 participants completed a 36-item online questionnaire. Data collected included injury history, rehabilitation strategies, RTS timelines, and responses to the Injury-Psychological Readiness to Return to Sport (I-PRRS) scale. A Random Forest machine learning algorithm was used to identify and rank potential injury risk factors. Results: Of the 360 respondents, 331 (92%) reported at least one injury, predominantly occurring during training sessions. The knee was the most frequently injured joint, and the action “attempting to pass guard” was the most reported mechanism. Most athletes (65%) returned to training within one month. BMI and age emerged as the most significant predictors of injury risk. Psychological readiness scores indicated moderate confidence, with the lowest levels associated with playing without pain. Conclusions: Injuries in BJJ are common, particularly affecting the knee. Psychological readiness, especially confidence in training without pain, plays a critical role in RTS outcomes. Machine learning models may aid in identifying individual risk factors and guiding injury prevention strategies. Full article

Background/Objectives: Antimicrobial consumption (AMC) surveillance and antimicrobial stewardship (AMS) constitute effective strategies to combat the increasing antimicrobial resistance rates worldwide. Post-Soviet countries (Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Moldova, the Russian Federation, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan) implemented various elements of AMC surveillance and AMS to different extents. The limited quantity and quality of data from post-Soviet countries make it difficult to assess health system performance; therefore, this region is a blind spot in global AMR monitoring. This systematic review assesses and characterises AMC surveillance and AMS implementation in post-Soviet countries. Methods: Evidence was compiled via a search in PubMed, Google Scholar, Embase, CyberLeninka, and Scopus. The eligibility criteria included AMC surveillance- and AMS-related papers in human health within defined regions and timelines. Some literature from the official websites of international and national health organisations was included in the search. Results: As a result of the searches, screening, and critical appraisal, three peer-reviewed publications and 31 documents were selected for analysis. Eleven out of fifteen countries with updated national action plans for combating antimicrobial resistance have defined AMC surveillance and AMS as strategic objectives. All 15 examined countries submitted antimicrobial consumption data to international networks and reported the existence of approved laws and regulations on antibiotic sales. However, disparities exist in the complexity of monitoring systems and AMS implementation between high-income and low-income countries in the region. Conclusions: This review provides key insights into the existing AMC surveillance and AMS implementation in former Soviet countries. Although the approach of this review lacks quantitative comparability, it provides a comprehensive qualitative framework for national-level AMC surveillance and AMS system assessment. Full article

The coherency matrix serves as a valuable tool for explaining the intricate details of various terrain targets. However, a significant challenge arises when analyzing ground targets with similar scattering characteristics in polarimetric synthetic aperture radar (PolSAR) target decomposition. Specifically, the overestimation of volume scattering (OVS) introduces ambiguity in characterizing the scattering mechanism and uncertainty in deciphering the scattering mechanism of large oriented built-up areas. To address these challenges, based on the generalized five-component decomposition (G5U), we propose a hierarchical extension of the G5U method, termed ExG5U, which incorporates orientation and phase angles into the matrix rotation process. The resulting transformed coherency matrices are then subjected to a five-component decomposition framework, enhanced with four refined volume scattering models. Additionally, we have reformulated the branch conditions to facilitate more precise interpretations of scattering mechanisms. To validate the efficacy of the proposed method, we have conducted comprehensive evaluations using diverse PolSAR datasets from Gaofen-3, Radarsat-2, and ESAR, covering varying data acquisition timelines, sites, and frequency bands. The findings indicate that the ExG5U method proficiently captures the scattering characteristics of ambiguous regions and shows promising potential in mitigating OVS, ultimately facilitating a more accurate portrayal of scattering mechanisms of various terrain types. Full article