Search Results (9,181)

Background/Objectives: Rapid pathogen identification is essential to optimize antimicrobial therapy and improve patient outcomes, particularly in severe infections. Syndromic molecular diagnostics have been introduced to overcome the limitations of conventional culture-based methods. This study evaluated the diagnostic performance and real-life implementation of BioFire^® FilmArray^® syndromic panels compared with routine microbiological diagnostics. Methods: A total of 955 clinical specimens collected between 2022 and June 2025 were retrospectively analyzed, including positive blood cultures (n = 400), lower respiratory tract samples (n = 309), cerebrospinal fluid (n = 158) and stool specimens (n = 88). FilmArray^® BCID2, Pneumonia Plus, Meningitis/Encephalitis and Gastrointestinal panels were performed on the Biofire Fimarray^® instrument according to clinical indication and compared with conventional culture-based identification and phenotypic antimicrobial susceptibility testing. Results: Overall diagnostic concordance between BioFire^® FilmArray^® syndromic panels and conventional methods was high across all specimen types, with the highest positive percent agreement (PPA) observed for bloodstream infections (97.7%) and gastrointestinal pathogens (100%). In respiratory samples, the Pneumonia Plus panel detected a considerable number of microorganisms that could not be identified by culture, including viral pathogens and fastidious bacteria. Molecular detection of antimicrobial resistance markers showed excellent concordance with phenotypic profiles, with 100% agreement for CTX-M, carbapenemases (KPC, NDM, OXA-48-like, IMP), and vanA/B, while lower concordance was observed for mecA/C in staphylococci. In parallel, semi-quantitative bacterial loads provided by the Pneumonia Plus panel showed a strong essential agreement with culture-based quantification (97.4%, ±1 log₁₀). Across all panels, syndromic testing significantly reduced diagnostic turnaround time. Conclusions: Syndromic molecular panels provide rapid and reliable simultaneous detection of pathogens, as well as early resistance marker detection, thereby supporting timely antimicrobial optimization and stewardship when integrated with conventional microbiological diagnostics. Full article

Type 1 diabetes (T1D) is a common chronic autoimmune disease in childhood, often presenting abruptly and frequently complicated by diabetic ketoacidosis at diagnosis. T1D develops through well-defined presymptomatic stages characterized by islet autoimmunity and progressive dysglycemia, offering a window for early identification. This narrative review summarizes current evidence on screening for T1D in children and adolescents, focusing on target populations, screening strategies, and methodological approaches for autoantibody detection. Data from major international programs involving familial, high-risk, and general population screening are discussed, highlighting their impact on reducing diabetic ketoacidosis at onset, improving metabolic outcomes, and facilitating structured follow-up and family education. Advances in assay technologies, including electrochemiluminescence, multiplex platforms, and novel ultrasensitive methods, have enhanced the feasibility and accuracy of large-scale screening. The review also examines the public health implications, cost-effectiveness, and ethical considerations of implementing population-based screening, particularly in light of emerging disease-modifying therapies such as teplizumab. Overall, available evidence supports screening as a meaningful strategy to shift T1D diagnosis from an acute emergency to a predictable clinical trajectory, with potential benefits extending from individual patient outcomes to healthcare system sustainability. Full article

Introduction: Urinary incontinence (UI) is one of the most common pelvic floor disorders after childbirth and depends on hormonal changes, anatomical damage that occurs after childbirth, muscle and connective tissue weakness, fascia and nerves. UI is distinguished into three subtypes, including stress urinary incontinence (SUI), urgent urinary incontinence (UUI) and mixed urinary incontinence (MUI). Aim: The purpose of this review is to collect and summarize the results of studies related to the risk factors of urinary incontinence, to disseminate this information to scientists so that this major issue can be prevented, identified and managed. Methodology: This review followed the methodology of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and PECO eligibility criteria were used. We included studies published up to 2025 and not before 2019. The review was limited to studies published within the last six years in order to reflect contemporary diagnostic criteria, assessment tools and current postpartum care practices related to urinary incontinence. We searched PubMed, Google Scholar and Scopus for studies concerning the relationship between risk factors and postpartum UI. Results: A total of 1321 citations were identified. Following our exclusion criteria, 36 papers were selected to identify the risk factors for UI. All the research focused on the associated factors of any type of urinary incontinence. Vaginal and instrumental delivery, obesity, maternal age and the neonate’s birth weight were the main risk factors. The multiparity and incontinence symptoms before and during pregnancy were also strong risk factors. Heterogeneity across studies in assessment tools, in outcome measures and timing of postpartum assessment are some of the limitations of the study. Restriction to English-language publications and the absence of protocol registration were some of the additional limitations of the study. Conclusions: This problem affects the inclusion of women in society, the family, limits social activities and even their ability to work. Detection of the type of urinary incontinence by healthcare professionals, lifestyle modifications, monitoring women’s body weight and encouraging them to follow a program of pelvic floor muscle exercises should be a priority for professionals. The strategy of developing prognostic models in the coming years will be the only way to ensure the early identification and follow-up of women at high risk for urinary disorders. Full article

Latent tuberculosis infection (LTBI) represents a major global health concern as it constitutes the principal reservoir for future tuberculosis (TB) disease. Its identification is particularly important in Bacille Calmette–Guérin (BCG)-vaccinated populations, where cross-reactivity of purified protein derivative limits the specificity of the tuberculin skin test and hampers targeted preventive therapy. Early Mycobacterium tuberculosis antigens encoded within the RD1 region, especially ESAT-6, CFP-10 and TB7.7, have enabled the development of antigen-specific interferon-gamma release assays (IGRAs) and recombinant skin tests with improved BCG-independent specificity. This narrative review integrates and critically appraises current evidence on the immunobiological properties of early and latency-associated antigens, the cellular mechanisms underlying T-cell-dependent immune reactivity, and the diagnostic performance of IGRAs and ESAT-6/CFP-10-based skin tests, rather than merely summarizing individual studies. Although these platforms rely on different assay principles (in vitro cytokine release versus in vivo delayed-type hypersensitivity), both measure antigen-specific T-cell memory and do not define the biological stage of infection or reliably distinguish latent from incipient or active TB. Across most adult populations, IGRAs demonstrate high specificity and acceptable sensitivity, whereas reduced sensitivity and higher rates of indeterminate results are observed in young children and immunocompromised individuals. ESAT-6/CFP-10-based skin tests show diagnostic accuracy comparable to IGRAs and may offer operational advantages in resource-limited settings. Latency-associated antigens and host biomarkers such as IP-10, together with multi-analyte immune signatures, represent promising avenues for improving diagnostic sensitivity and prognostic stratification but currently lack sufficient validation for routine clinical use. Overall, RD1-encoded antigens remain central to LTBI immunodiagnosis, while future research should focus on developing stage-resolving and prognostic biomarkers, optimized antigen panels, and standardized interpretive frameworks. Full article

Background: Preterm birth remains a major cause of perinatal morbidity and long-term developmental complications. Existing prediction methods often lack individualized assessment and have limited capability to integrate multi-source maternal–fetal information. This study aims to develop a personalized preterm birth risk prediction model and to construct a visual, interactive digital twin platform that enhances clinical communication and supports early risk identification. Methods: A total of 1157 structured clinical records collected from 2020 to 2024 were preprocessed through automated feature typing, missing-value handling, and normalization. Two complementary machine-learning models—FT-Transformer and Light Gradient Boosting Machine (LightGBM)—were trained and calibrated to produce probabilities. Their outputs were fused using a Stacking Logistic Regression framework to improve prediction stability and calibration. A 3D visualization module was developed using 3ds Max, PyQt6, and PyVista to generate personalized uterine–fetal models based on fetal position, placental location, and Biparietal Diameter (BPD), enabling synchronized display of prediction results. Results: The fused model achieved an AUC of 0.820, PR-AUC of 0.405, a Brier score of 0.040, and an expected calibration error (ECE) of 3.39 × 10⁻³, demonstrating superior discrimination and probability reliability compared with single models. The interactive platform supports real-time data input, risk prediction, and adaptive 3D rendering, providing clear and intuitive visual feedback for clinical interpretation. Conclusions: The integration of machine learning fusion and digital twin visualization enables individualized assessment of preterm birth risk. The system improves model accuracy, enhances interpretability, and offers a practical tool for clinical follow-up, risk counseling, and maternal health education. Full article

Forensic DNA profiling commonly relies on polymerase chain reaction (PCR) amplification followed by capillary electrophoresis (CE) or massively parallel sequencing (MPS), which requires expensive, laboratory-based equipment that depends on a stable power supply and is unsuitable for field applications. Here, we present a proof-of-concept assay that uses recombinase polymerase amplification (RPA) combined with exo probe detection for rapid, isothermal genotyping of insertion–deletion (InDel) markers. To the best of our knowledge, this study represents the first demonstration of forensic DNA typing using RPA coupled with exo probes. The reaction proceeds at 39 °C and combines amplification and detection in a single 20 min step. Thirteen DNA samples were genotyped in triplicate across eight InDel loci using allele-specific fluorescent probes. Genotypes were derived from differential endpoint fluorescence between matched and mismatched probes. Compared with benchmark genotyping, 97.07% of genotypes (n = 307) were correct at 1 ng DNA input. Accurate profiles were reliably obtained for DNA inputs as low as 250 pg, and partial profiles were still detectable at 31 pg. The results demonstrate that RPA-based InDel genotyping is fast, sensitive, and reproducible. With further optimization, such as refined probe design and selection of robust loci, the assay has clear potential to achieve complete accuracy and to be integrated into portable lab-on-a-chip platforms for rapid, field-deployable forensic identification. Full article

This study developed an integrated vision–actuation system for precision weeding in indoor soil bin environments, with cabbage as a case example. The system integrates lightweight object detection, 3D co-ordinate mapping, path planning, and a three-axis synchronized conveyor-type actuator to enable precise weed identification and automated removal. By integrating ECA and CBAM attention mechanisms into YOLO11, we developed the YOLO11-WeedNet model. This integration significantly enhanced the detection performance for small-scale weeds under complex lighting and cluttered backgrounds. Based on the optimal model performance achieved during experimental evaluation, the model achieved 96.25% precision, 86.49% recall, 91.10% F1-score, and a mean Average Precision (mAP@0.5) of 91.50% calculated across two categories (crop and weed). An RGB-D fusion localization method combined with a protected-area constraint enabled accurate mapping of weed spatial positions. Furthermore, an enhanced Artificial Hummingbird Algorithm (AHA⁺) was proposed to optimize the execution path and reduce the operating trajectory while maintaining real-time performance. Indoor soil bin tests showed positioning errors of less than 8 mm on the X/Y axes, depth control within ±1 mm on the Z-axis, and an average weeding rate of 88.14%. The system achieved zero contact with cabbage seedlings, with a processing time of 6.88 s per weed. These results demonstrate the feasibility of the proposed system for precise and automated weeding at the cabbage seedling stage. Full article

Pasteurella multocida is a pathogen of numerous mammal and bird species. Based on capsular antigens, five capsular types of P. multocida (A, B, D, E, and F) are distinguished. The aim of this study was to evaluate the usefulness of multiplex PCR and MALDI-TOF MS for the identification and capsular typing of P. multocida strains isolated from rabbits. A total of 115 field strains previously classified as P. multocida, isolated in Poland between 1999 and 2020, were analysed. Multiplex PCR was applied for simultaneous species identification and determination of capsular types. Most strains belonged to capsular type A (87.8%), while capsular types D (8.7%) and F (3.5%) were detected less frequently. The examined strains were subsequently identified by MALDI-TOF MS, which correctly assigned all strains to the species P. multocida. The results demonstrate that multiplex PCR is a rapid and reliable alternative to conventional species identification and serological capsular typing of P. multocida. In addition, MALDI-TOF MS proved to be a valuable tool for accurate species-level identification. The application of these methods in routine clinical microbiology laboratories may significantly improve the speed and reliability of P. multocida identification. Full article

Pseudomonas aeruginosa is a Gram-negative pathogen with a remarkable capacity to acquire multiple resistance mechanisms, severely limiting current therapeutic options. Consequently, the identification of new antimicrobial agents remains a critical priority. In this study, an integrated in silico-guided strategy was applied to identify small molecules with antibacterial potential against P. aeruginosa, targeting the quorum-sensing regulator LasR (PDB ID: 2UV0) and elastase (PDB ID: 1U4G). Pharmacophore modeling was performed for both targets, followed by ligand-based virtual screening, structure-based virtual screening (SBVS), and MM-GBSA (Molecular Mechanics-Generalized Born Surface Area) binding free energy calculations. Top-ranked compounds based on predicted binding affinity were selected for in vitro cytotoxicity and antibacterial evaluation. Antimicrobial activity was assessed against three P. aeruginosa strains: an American Type Culture Collection (ATCC) reference strain, a clinically susceptible isolate, and an extensively drug-resistant (XDR) clinical isolate. SBVS yielded docking scores ranging from −6.96 to −12.256 kcal/mol, with MM-GBSA binding free energies between −18.554 and −88.00 kcal/mol. Minimum inhibitory concentration (MIC) assays revealed that MolPort-001-974-907, MolPort-002-099-073, MolPort-008-336-135, and MolPort-008-339-179 exhibited MIC values of 62.5 µg/mL against the ATCC strain, indicating weak-to-moderate antibacterial activity consistent with early-stage hit compounds. MolPort-008-336-135 showed the most favorable activity against the clinically susceptible isolate, with an MIC of 62.5 µg/mL, while maintaining HepG2 cell viability above 70% at this concentration and an half-maximal inhibitory concentration (IC₅₀) greater than 500 µg/mL. In contrast, all tested compounds displayed MIC values above 62.5 µg/mL against the XDR isolate, reflecting limited efficacy against highly resistant strains. Overall, these results demonstrate the utility of in silico-driven approaches for the identification of antibacterial hit compounds targeting LasR and elastase, while highlighting the need for structure–activity relationship optimization to improve potency, selectivity, and activity against multidrug-resistant P. aeruginosa. Full article

Shiga toxin-producing Escherichia coli (STEC) are major foodborne pathogens that can cause severe human disease. Identifying molecular markers associated with pathogenicity is essential to improve detection and to better understand virulence mechanisms. In this study, 35,828 E. coli genomes available in a public database were analyzed with the purpose of identifying STEC genes encoding proteins secreted to the outer membrane or into the extracellular space. The strains belong to six different sequence types (STs): ST442, ST297, ST1131, ST2217, ST2387, and ST2520. Comparative genomics allowed identification of 155 genes that were initially evaluated as potential virulence-associated candidates. Among these, a hydrolase-encoding gene showed a wide distribution and a significant association with STEC genomes versus non-STEC genomes (81.6% in the STEC group and 15.8% in the non-STEC group). The protein product is predicted to be released into the extracellular space and, according to examples in other bacterial pathogens, may contribute to STEC virulence. Altogether, our findings highlight this hydrolase as a relevant molecular marker that could complement current genetic screening schemes and support the design of novel control strategies against STEC infections. Full article

Spinocerebellar ataxia type 2 (SCA2) is an autosomal dominant neurodegenerative disorder caused by a pathogenic CAG trinucleotide repeat expansion in the ATXN2 gene. At-risk couples can embark on unaffected pregnancies through preimplantation genetic testing of monogenic disorders (PGT-M) of SCA2, which should involve accurate repeat expansion detection together with risk haplotype tracking using informative linked markers. Two couples underwent SCA2 PGT-M involving analysis of whole genome amplified embryonic trophectoderm cells by ATXN2 (CAG)n triplet-primed PCR (TP-PCR) and linkage-based risk allele genotyping using customized markers. To simplify and expedite the identification of informative markers for future PGT-M cases, putative microsatellite markers closely linked to ATXN2 were initially screened for polymorphism using a small set of anonymous DNA samples obtained from Coriell Cell Repository. Shortlisted markers with high polymorphism likelihood were then multiplexed in a single-tube reaction and genotyped on 190 anonymous DNA samples to determine their polymorphic information content. Across both SCA2 PGT-M clinical cases, the linked marker genotypes corroborated the TP-PCR results, allowing clear differentiation between unaffected and affected embryos. In both cases, transfer of an unaffected embryo led to a successful pregnancy and live birth of a healthy baby. In silico mining, filtering, and curation identified 287 microsatellites located within 1.65 Mb of either side of the ATXN2 CAG repeat. Of these, eight upstream and nine downstream polymorphic markers were successfully co-amplified in a single-tube assay and demonstrated high overall heterozygosity in both Chinese and Caucasian populations. Conclusion: To ensure high diagnostic accuracy for PGT-M of SCA2, we developed a heptadecaplex microsatellite marker panel for haplotype-based linkage analysis to complement TP-PCR-based direct detection of the ATXN2 CAG repeat. The panel can rapidly identify informative markers from virtually any couple, and it works equally well on MDA-amplified DNAs for embryonic haplotype analysis. Full article

Passive radio frequency identification (RFID) sensing systems integrate wireless energy transfer with information identification. However, conventional passive RFID systems still face three key challenges in practical applications: low RF energy harvesting efficiency, high power consumption of sensor loads, and high complexity of tag anti-collision algorithms. To address these issues, this paper proposes a hardware–software co-optimized RFID sensor system. For hardware, low threshold RF Schottky diodes are selected, and an input inductor is introduced into the voltage multiplier rectifier to boost the signal amplitude, thereby enhancing the radio frequency to direct current (RF-DC) energy conversion efficiency. In terms of loading, a low-power management strategy is implemented for the power supply and control logic of the sensor node to minimize the overall system energy consumption. For algorithmic implementation, a Dual-Threshold Stepped Dynamic Frame Slotted ALOHA (DTS-DFSA) anti-collision algorithm is proposed, which adaptively adjusts the frame length based on the observed collision ratio, eliminating the need for complex tag population estimation. The algorithm features low computational complexity and is well suited for resource constrained embedded platforms. Through simulation validation, we compare the conversion efficiency of the RF energy harvesting circuit before and after improvement, the current of the sensor load in active and idle states, and the performance of the proposed algorithm against the low-complexity DFSA (LC-DFSA). The results show that the maximum conversion efficiency of the improved RF energy harvesting circuit has increased from 60.56% to 68.69%; specifically, the sensor load current drastically drops from approximately 2.0 <!-- MathType@Translator@5@5@MathML2 (no namespace).tdl@MathML 2.0 (no namespace)@ --> Full article

Cancer is a heterogeneous disease at the cellular level and analyzing the genetic and molecular profile is essential for targeted therapy. Cancer cells continue to mutate, often resulting in drug resistance. In addition, cancers such as triple-negative breast cancer (TNBC) lack the target proteins used in some of the most effective therapies. This necessitates the identification of novel target proteins and biomarkers for effective treatment strategies. Ubiquitin E3 ligases are often differentially expressed in cancer cells, and numerous anticancer agents have been developed to inhibit them. SMURF2 is an E3 ligase that is differentially expressed in multiple cancer types. Although inhibiting upregulated SMURF2 may be strategically straightforward, enhancing the downregulated gene is often difficult. In addition, because E3 ligases ubiquitinate a variety of substrate proteins, targeting SMURF2 requires detailed analysis to achieve anticancer effect. This review discusses the dual role of SMURF2 in carcinogenesis and addresses the complex context-dependent function of SMURF2 in the various cellular pathways. In addition, resistance to existing cancer therapy related to SMURF2 and sensitivity mechanisms is discussed. Lastly, theranostic strategies for anticancer agents and biomarker development are suggested. Full article

Low-resistivity reservoirs characterized by weak log contrasts are highly concealed and therefore difficult to detect using conventional oil–water discrimination methods. Recent exploration and development indicate that low-resistivity reservoirs are widely developed in the Triassic Chang 3 Member of the Zhenbei area, Ordos Basin. However, contrasting tectonic evolution associated with the Tianhuan Depression and complex lithologic assemblages in the western and eastern sectors have resulted in complicated hydrocarbon migration and accumulation processes. In this study, integrated well-log and geochemical data were used to systematically investigate the genesis of low-resistivity reservoirs in the Chang 3 Member and to establish oil–water discrimination charts. Three-dimensional seismic flattening was applied to restore the Late Jurassic paleostructure of the western Chang 3 Member and to analyze its tectonic evolution. Reservoir petrology and pore–throat architecture in the western and eastern areas were comparatively examined using thin-section petrography, field-emission scanning electron microscopy (FESEM), and high-pressure mercury intrusion. Results indicate that the development of low-resistivity reservoirs in the Chang 3 Member is primarily controlled by highly saline formation water and elevated bound-water saturation. Based on these controls, the invasion factor–acoustic transit time cross-plot and the apparent spontaneous potential difference (ΔSP) method effectively discriminate oil- and water-bearing intervals in a total of 25 wells within the study area. Paleostructural restoration reveals that the western Chang 3 Member has undergone a tectonic inversion from a west-high–east-low configuration since the Late Jurassic to the present-day east-high–west-low geometry. Oil–source correlation indicates that hydrocarbons in the Chang 3 reservoirs were mainly derived from the underlying Chang 7 source rocks, whereas the bimodal distribution of fluid-inclusion homogenization temperatures suggests that the reservoirs experienced two distinct charging episodes. Integrated analysis suggests that tectonic inversion during the Yanshanian movement, combined with multistage hydrocarbon charging, led to secondary migration and partial destruction of early-formed reservoirs in the western area, resulting in predominantly scattered accumulations. In contrast, the eastern area experienced relatively limited tectonic modification, and laterally extensive accumulations are controlled by Type I–III lithologic–structural traps formed by the Chang 3 reservoir interval and its overlying strata. These findings provide an important geological basis for the identification of low-contrast reservoirs and for the exploration and development of hydrocarbon accumulations that are jointly controlled by tectonic evolution and lithologic heterogeneity. Full article

Veryovkina Cave is the world’s deepest known cave (2212 m deep). It is located in the Arabika Massif of Gagra Mountain in the Western Caucasus. Its microbiome remains unknown because of difficulties in access. Ten sediment samples were collected at vertical depths ranging from 300 m to 2204 m; they varied by substrate type, moisture content, and visitor accessibility. Total microbial DNA was isolated, and 16S ribosomal gene metabarcoding was applied for taxonomic identification. Seven samples showed reliable content, whereas three samples indicated no recoverable reads. Proteobacteria, Acidobacteria, and Actinobacteria were the most abundant phyla in total. Depth stratification of microbiota showed that (1) shallow wet clays were dominated by Acidimicrobia and Actinobacteria; (2) mid-depth wet clays showed the highest abundance of Nitrospira, Betaproteobacteria, and Vicinamibacter; and (3) deep, dry substrates were dominated by Thermoleophilia and Rubrobacteria. Multivariate analyses showed that substrate type and moisture tended to explain more variation in microbial abundance than depth or human activity. We demonstrate the presence of distinct ecological niches within the cave ecosystem, which emphasizes the role of local conditions in shaping microbial diversity. Full article