Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Identification of immune cell infiltration and effective biomarkers of polycystic ovary syndrome by bioinformatics analysis

Abstract Background Patients with polycystic ovary syndrome (PCOS) exhibit a chronic inflammatory state, which is often accompanied by immune, endocrine, and metabolic disorders. Clarification of the pathogenesis of PCOS and exploration of specific biomarkers from the perspective of immunology by evaluating the local infiltration of immune cells in the follicular microenvironment may provide critical insights into disease pathogenesis. Methods In this study, we evaluated immune cell subsets and gene expression in patients with PCOS using data from the Gene Expression Omnibus database and single-sample gene set enrichment analysis. Results In total, 325 differentially expressed genes were identified, among which TMEM54 and PLCG2 (area under the curve = 0.922) were identified as PCOS biomarkers. Immune cell infiltration analysis showed that central memory CD4+ T cells, central memory CD8+ T cells, effector memory CD4+ T cells, γδ T cells, and type 17 T helper cells may affect the occurrence of PCOS. In addition, PLCG2 was highly correlated with γδ T cells and central memory CD4+ T cells. Conclusions Overall, TMEM54 and PLCG2 were identified as potential PCOS biomarkers by bioinformatics analysis. These findings established a basis for further exploration of the immunological mechanisms of PCOS and the identification of therapeutic targets

A Brief Analysis of Traditional Chinese Medical Elongated Needle Therapy on Acute Spinal Cord Injury and Its Mechanism

Acute spinal cord injury is one of the most common and complicated diseases among human spinal injury. We aimed to explore the effect of point-through-point acupuncture therapy with elongated needles on acute spinal cord injury in rabbits and its possible mechanism. Adult rabbits were randomly divided into a model group, elongated needle therapy group, and blank group. Immunohistochemical staining showed that the protein levels of Fas and caspase-3 in the model group were significantly higher than those in the blank group at each time point (P<0.05) and significantly lower than those in the elongated needle therapy group on the 3rd and 5th days after operation (P<0.05). RT-PCR showed that Fas and caspase-3 mRNA levels in the model group and elongated needle therapy group were significantly higher than those in the blank group (P<0.05, 0.01). The mRNA levels of Fas and caspase-3 in the elongated needle therapy group were significantly lower than those in model group on the 3rd day (P<0.05, 0.01). Therefore, we confirmed that elongated needle therapy has an obvious effect on acute spinal cord injury in rabbits. Its mechanism is made possible by inhibiting the expression of the Fas→caspase-3 cascade, thereby inhibiting cell apoptosis after spinal cord injury

A Hierarchical Federated Learning-Based Intrusion Detection System for 5G Smart Grids

As the core component of smart grids, advanced metering infrastructure (AMI) provides the communication and control functions to implement critical services, which makes its security crucial to power companies and customers. An intrusion detection system (IDS) can be applied to monitor abnormal information and trigger an alarm to protect AMI security. However, existing intrusion detection models exhibit a low performance and are commonly trained on cloud servers, which pose a major threat to user privacy and increase the detection delay. To solve these problems, we present a transformer-based intrusion detection model (Transformer-IDM) to improve the performance of intrusion detection. In addition, we integrate 5G technology into the AMI system and propose a hierarchical federated learning intrusion detection system (HFed-IDS) to collaboratively train Transformer-IDM to protect user privacy in the core networks. Finally, extensive experimental results using a real-world intrusion detection dataset demonstrate that the proposed approach is superior to other existing approaches in terms of detection accuracy and communication cost for an IDS