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Rapid identification of microorganisms with a high sensitivity and selectivity is of great interest in many fields, primarily in clinical diagnosis, environmental monitoring, and the food industry. For over the past decades, a surface-enhanced Raman scattering (SERS)-based detection platform has been extensively used for bacterial detection, and the effort has been extended to clinical, environmental, and food samples. In contrast to other approaches, such as enzyme-linked immunosorbent assays and polymerase chain reaction, SERS exhibits outstanding advantages of rapid detection, being culture-free, low cost, high sensitivity, and lack of water interference. This review aims to cover the development of SERS-based methods for bacterial detection with an emphasis on the source of the signal, techniques used to improve the limit of detection and specificity, and the application of SERS in high-throughput settings and complex samples. The challenges and advancements with the implementation of artificial intelligence (AI) are also discussed.

Vitamin D is a crucial element in bone metabolism and plays a role in innate immunity and inflammation suppression. Deficiency in this vitamin leads to disturbances in many biological functions, including the lipid profile. The decrease in HDL levels is associated with disruptions in its function and dynamic modifications in its components, such as apolipoproteins, including ApoM, ApoA-1, and ApoD. Consequently, the anti-inflammatory potential of HDL and HDL-associated proteins is reduced, resulting in heightened inflammation. However, the relationship between modifications in lipid profile, apolipoproteins, inflammation, and vitamin D remains unclear. This review highlights the connection between vitamin D status and its possible effects on the lipid profile, specifically HDL-associated proteins.

Transdermal vaccination route using biodegradable microneedles is a rapidly progressing field of research and applications. The fear of painful needles is one of the primary reasons most people avoid getting vaccinated. Therefore, developing an alternative pain-free method of vaccination using microneedles has been a significant research area. Microneedles comprise arrays of micron-sized needles that offer a pain-free method of delivering actives across the skin. Apart from being pain-free, microneedles provide various advantages over conventional vaccination routes such as intramuscular and subcutaneous. Microneedle vaccines induce a robust immune response as the needles ranging from 50 to 900 μm in length can efficiently deliver the vaccine to the epidermis and the dermis region, which contains many Langerhans and dendritic cells. The microneedle array looks like band-aid patches and offers the advantages of avoiding cold-chain storage and self-administration flexibility. The slow release of vaccine antigens is an important advantage of using microneedles. The vaccine antigens in the microneedles can be in solution or suspension form, encapsulated in nano or microparticles, and nucleic acid-based. The use of microneedles to deliver particle-based vaccines is gaining importance because of the combined advantages of particulate vaccine and pain-free immunization. The future of microneedle-based vaccines looks promising however, addressing some limitations such as dosing inadequacy, stability and sterility will lead to successful use of microneedles for vaccine delivery. This review illustrates the recent research in the field of microneedle-based vaccination.

Neisseria gonorrhoeae is a strict human pathogen that causes the sexually transmitted infection termed gonorrhea. The gonococcus can survive extracellularly and intracellularly, but in both environments the bacteria must acquire iron from host proteins for survival. However, upon infection the host uses a defensive response by limiting the bioavailability of iron by a number of mechanisms including the enhanced expression of hepcidin, the master iron-regulating hormone, which reduces iron uptake from the gut and retains iron in macrophages. The host also secretes the antibacterial protein NGAL, which sequesters bacterial siderophores and therefore inhibits bacterial growth. To learn whether intracellular gonococci can subvert this defensive response, we examined expression of host genes that encode proteins involved in modulating levels of intracellular iron. We found that N. gonorrhoeae can survive in association (tightly adherent and intracellular) with monocytes and macrophages and upregulates a panel of its iron-responsive genes in this environment. We also found that gonococcal infection of human monocytes or murine macrophages resulted in the upregulation of hepcidin, NGAL, and NRAMP1 as well as downregulation of the expression of the gene encoding the short chain 3-hydroxybutyrate dehydrogenase (BDH2); BDH2 catalyzes the production of the mammalian siderophore 2,5-DHBA involved in chelating and detoxifying iron. Based on these findings, we propose that N. gonorrhoeae can subvert the iron-limiting innate immune defenses to facilitate iron acquisition and intracellular survival.

The growing concern of pediatric mortality demands heightened preparedness in clinical settings, especially within intensive care units (ICUs). As respiratory-related admissions account for a substantial portion of pediatric illnesses, there is a pressing need to predict ICU mortality in these cases. This study based on data from 1188 patients, addresses this imperative using machine learning techniques and investigating different class balancing methods for pediatric ICU mortality prediction. This study employs the publicly accessible “Paediatric Intensive Care database” to train, validate, and test a machine learning model for predicting pediatric patient mortality. Features were ranked using three machine learning feature selection techniques, namely Random Forest, Extra Trees, and XGBoost, resulting in the selection of 16 critical features from a total of 105 features. Ten machine learning models and ensemble techniques are used to make accurate mortality predictions. To tackle the inherent class imbalance in the dataset, we applied a unique data partitioning technique to enhance the model's alignment with the data distribution. The CatBoost machine learning model achieved an area under the curve (AUC) of 72.22%, while the stacking ensemble model yielded an AUC of 60.59% for mortality prediction. The proposed subdivision technique, on the other hand, provides a significant improvement in performance metrics, with an AUC of 85.2% and an accuracy of 89.32%. These findings emphasize the potential of machine learning in enhancing pediatric mortality prediction and inform strategies for improved ICU readiness.

Background Monocyte-to-HDL Ratio (MHR) biomarker reflects monocyte-driven inflammation and HDL’s anti-inflammatory properties. MHR’s reference ranges and prognostic utility remain undefined. We establish normal MHR reference ranges and examine its association with inflammatory diseases and mortality. Methods Using NHANES data (1999–2018, 2021–2023), two sets of sex-specific MHR reference ranges were generated using two healthy adult populations (monocyte count: 6,757; monocyte percentage: 6,817). Further analyses utilized MHR by monocyte count for more straightforward interpretation. Adjusted associations between MHR and inflammatory diseases were assessed in 49,929 adults, and disease-specific mortality in 35,781. Results The 2.5th–97.5th percentiles for MHR by monocyte count were 0.175 (90% CI: 0.167–0.184) to 0.709 (90% CI: 0.690–0.727) in males and 0.135 (90% CI: 0.130–0.140) to 0.511 (90% CI: 0.503–0.520) in females, with similar trends for MHR by monocyte percentage. High MHR was most strongly associated with diabetes (aOR = 1.76, p  < 0.001) and cardiovascular disease (aOR = 1.69, p  < 0.001), while mortality risk was highest for kidney disease (aHR = 3.13, p  < 0.001) and diabetes (aHR = 2.26, p  < 0.001). Conclusion MHR is a feasible and accessible biomarker of inflammation and lipid dysregulation that can be derived from routine laboratory tests and shows strong associations with cardiometabolic diseases and disease-related mortality.

Background Evidence from the literature suggests that vitamin D has indirect antimicrobial effects and may be associated with a reduced risk of infections. This study aimed to systematically evaluate the association between vitamin D deficiency and the risk of developing surgical site infections (SSIs). Methods All types of studies will be included in the systematic review. Up to December 2025, Medline, Embase, Cochrane, Web of Science, CINAHL, Google Scholar, ClinicalTrials.gov, WHO-ICTRP, Cochrane Central Register of Controlled Trials, and relevant citations will be searched. The primary outcome will be the development of SSIs. Study selection will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. Meta-analysis will be performed using bias-adjusted inverse variance heterogeneity methods. The risk of bias will be assessed using the MASTER scale, and the certainty of evidence will be determined using the GRADE framework. Discussion Although many risk factors for SSIs have been identified, the role of vitamin D remains unclear. SSIs impose a significant burden on patients and healthcare systems. This systematic review and meta-analysis aims to comprehensively evaluate the impact of vitamin D on SSI risk by including all relevant studies without language restrictions, using rigorous methodology in accordance with the Cochrane Handbook and PRISMA guidelines. If an association is established, preoperative screening and optimization of vitamin D levels could help reduce the burden of SSIs. Systematic review registration PROSPERO registration number 427175.

Common carotid intima-media thickness (CIMT) is a common measure of atherosclerosis, often assessed through carotid ultrasound images. However, the use of deep learning methods for medical image analysis, segmentation and CIMT measurement in these images has not been extensively explored. This study aims to evaluate the performance of four recent deep learning models, including a convolutional neural network (CNN), a self-organizing operational neural network (self-ONN), a transformer-based network and a pixel difference convolution-based network, in segmenting the intima-media complex (IMC) using the CUBS dataset, which includes ultrasound images acquired from both sides of the neck of 1088 participants. The results show that the self-ONN model outperforms the conventional CNN-based model, while the pixel difference- and transformer-based models achieve the best segmentation performance.