Targeting phosphodiesterase 4 as a potential therapy for Parkinson's disease: a review.

Phosphodiesterases (PDEs) have become a promising therapeutic target for various disorders. PDEs are a vast and diversified family of enzymes that degrade cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP), which have several biochemical and physiological functions. Phosphodiesterase 4 (PDE4) is the most abundant PDE in the central nervous system (CNS) and is extensively expressed in the mammalian brain, where it catalyzes the hydrolysis of intracellular cAMP. An alteration in the balance of PDE4 and cAMP results in the dysregulation of different biological mechanisms involved in neurodegenerative diseases. By inhibiting PDE4 with drugs, the levels of cAMP inside the cells could be stabilized, which may improve the symptoms of mental and neurological disorders such as memory loss, depression, and Parkinson's disease (PD). Though numerous studies have shown that phosphodiesterase 4 inhibitors (PDE4Is) are beneficial in PD, there are presently no approved PDE4I drugs for PD. This review presents an overview of PDE4Is and their effects on PD, their possible underlying mechanism in the restoration/protection of dopaminergic cell death, which holds promise for developing PDE4Is as a treatment strategy for PD. Methods on how these drugs could be effectively delivered to develop as a promising treatment for PD have been suggested.

PDE-LEARN: Using deep learning to discover partial differential equations from noisy, limited data.

In this paper, we introduce PDE-LEARN, a novel deep learning algorithm that can identify governing partial differential equations (PDEs) directly from noisy, limited measurements of a physical system of interest. PDE-LEARN uses a Rational Neural Network, U, to approximate the system response function and a sparse, trainable vector, ξ, to characterize the hidden PDE that the system response function satisfies. Our approach couples the training of U and ξ using a loss function that (1) makes U approximate the system response function, (2) encapsulates the fact that U satisfies a hidden PDE that ξ characterizes, and (3) promotes sparsity in ξ using ideas from iteratively reweighted least-squares. Further, PDE-LEARN can simultaneously learn from several data sets, allowing it to incorporate results from multiple experiments. This approach yields a robust algorithm to discover PDEs directly from realistic scientific data. We demonstrate the efficacy of PDE-LEARN by identifying several PDEs from noisy and limited measurements.

Polymerizable deep eutectic solvent treated lignocellulose: Green strategy for synergetic production of tough strain sensing elastomers and nanocellulose.

Liquid free ion-conductive elastomers (ICEs) have demonstrated promising potential in various advanced application scenarios including sensor, artificial skin, and human-machine interface. However, ICEs that synchronously possess toughness, adhesiveness, stability, and anti-bacterial capability are still difficult to achieve yet highly demanded. Here, a one-pot green and sustainable strategy was proposed to fabricate multifunctional ICEs by extracting non-cellulose components (mainly lignin and hemicellulose) from lignocellulose with polymerizable deep eutectic solvents (PDES) and the subsequent in-situ photo-polymerization process. Ascribing to the uniform dispersion of non-cellulose components in PDES, the resultant ICEs demonstrated promising mechanical strength (a tensile strength of ~1200 kPa), high toughness (~9.1 MJ m-3), favorable adhesion (a lap-shear strength up to ~61.5 kPa toward metal), conducive stabilities, and anti-bacterial capabilities. With the help of such advantages, the ICEs exhibited sensitive (a gauge factor of ~23.5) and stable (~4000 cycles) performances in human motion and physiological signal detection even under sub-zero temperatures (e.g., -20 °C). Besides, the residue cellulose can be mechanically isolated into nanoscale fibers, which matched the idea of green chemistry.

A mini-review: phosphodiesterases in charge to balance intracellular cAMP during T-cell activation.

T-cell activation is a pivotal process of the adaptive immune response with 3',5'-cyclic adenosine monophosphate (cAMP) as a key regulator of T-cell activation and function. It governs crucial control over T-cell differentiation and production of pro-inflammatory cytokines, such as IFN-γ. Intriguingly, levels of intracellular cAMP differ between regulatory (Treg) and conventional T-cells (Tcon). During cell-cell contact, cAMP is transferred via gap junctions between these T-cell subsets to mediate the immunosuppressive function of Treg. Moreover, the activation of T-cells via CD3 and CD28 co-stimulation leads to a transient upregulation of cAMP. Elevated intracellular cAMP levels are balanced precisely by phosphodiesterases (PDEs), a family of enzymes that hydrolyze cyclic nucleotides. Various PDEs play distinct roles in regulating cAMP and cyclic guanosine monophosphate (cGMP) in T-cells. Research on PDEs has gained growing interest due to their therapeutic potential to manipulate T-cell responses. So far, PDE4 is the best-described PDE in T-cells and the first PDE that is currently targeted in clinical practice to treat autoimmune diseases. But also, other PDE families harbor additional therapeutic potential. PDE2A is a dual-substrate phosphodiesterase which is selectively upregulated in Tcon upon activation. In this Mini-Review, we will highlight the impact of cAMP regulation on T-cell activation and function and summarize recent findings on different PDEs regulating intracellular cAMP levels in T-cells.

Evidence of binding between diethylstilbestrol (DES) and the goldfish (Carassius auratus) membrane progesterone receptor α.

BACKGROUND: In a previous study, diethylstilbestrol (DES) was shown to induce oocyte maturation in fish. In the present study, the interaction of DES on goldfish membrane progesterone receptor α (GmPRα) was investigated using a competitive binding assay with radiolabeled steroids. The results indicate that DES exerts its effects on membrane progesterone receptor alpha (mPRα) and induces oocyte maturation through nongenomic steroid mechanisms. This study provides empirical data that demonstrate the binding between DES and GmPRα. METHODS: Binding of DES to GmPRα was achieved by using radiolabeled DES and recombinant GmPRα expressed in culture cells or purified GmPRα proteins that coupled to graphene quantum dots (GQDs). Additionally, the competitive binding of fluorescently labeled progesterone to GmPRα-expressing cells was evaluated. RESULTS: Although significant nonspecific binding of radiolabeled DES to the cell membrane that expresses GmPRα has been observed, specific binding of DES to GmPRα has been successfully identified in the presence of digitonin. Furthermore, the specific binding of DES to GmPRα was confirmed by a binding assay using GQD-GmPRα. The radiolabeled DES was shown to bind to GQD-GmPRα. Additionally, the competition for the binding of fluorescently labeled progesterone to GmPRα-expressing cells was achieved with the DES. CONCLUSIONS: The results of the experiments revealed that DES binds to GmPRα. Thus, it can be concluded that DES induces goldfish oocyte maturation by binding to GmPRα.

Protocol for modeling and simulating lithiation-induced stress in largely deformed spherical nanoparticles using COMSOL.

Here, we present a finite element method-based scheme for solving coupled partial differential equations (PDEs) for the analysis of lithiation-induced stress in largely deformed spherical nanoparticles via the PDE module in COMSOL. We describe steps for software installation and setting PDEs, initial/boundary conditions, and mesh parameters. We then detail procedures for dividing the mesh and analyzing lithium trapping during electrochemical cycling. This protocol can also be extended to analyze a wide range of problems involving diffusion-induced stress. For complete details on the use and execution of this protocol, please refer to Li et al.1.

Multi-material 3D printed eutectogel microneedle patches integrated with fast customization and tunable drug delivery.

Microneedle patches are emerging multifunctional platforms for transdermal diagnostics and drug delivery. However, it still remains challenging to develop smart microneedles integrated with customization, sensing, detection and drug delivery by 3D printing strategy. Here, we present an innovative but facile strategy to rationally design and fabricate multifunctional eutectogel microneedle (EMN) patches via multi-material 3D printing. Polymerizable deep eutectic solvents (PDES) were selected as printing inks for rapid one-step fabrication of 3D printing functional EMN patches due to fast photopolymerization rate and ultrahigh drug solubility. Moreover, stretchable EMN patches incorporating rigid needles and flexible backing layers were easily realized by changing PDES compositions of multi-material 3D printing. Meanwhile, we developed multifunctional smart multi-material EMN patches capable of performing wireless monitoring of body movements, painless colorimetric glucose detection, and controlled transdermal drug delivery. Thus, such multi-material EMN system could provide an effective platform for the painless diagnosis, detection, and therapy of a variety of diseases.

Plant-derived exosomes: a green approach for cancer drug delivery.

Plant-derived exosomes (PDEs) are natural extracellular vesicles (EVs). In the current decade, they have been highlighted for cancer therapeutic development. Cancer is a global health crisis and it requires an effective, affordable, and less side effect-based treatment. Emerging research based on PDEs suggests that they have immense potential to be considered as a therapeutic option. Research evidences indicate that PDEs' internal molecular cargos show impressive cancer prevention activity with less toxicity. PDEs-based drug delivery systems overcome several limitations of traditional drug delivery tools. Extraction of PDEs from plant sources employ diverse methodologies, encompassing ultracentrifugation, immunoaffinity, size-based isolation, and precipitation, each with distinct advantages and limitations. The core constituents of PDEs comprise of lipids, proteins, DNA, and RNA. Worldwide, a few clinical trials on plant-derived exosomes are underway, and regulatory affairs for their use as therapeutic agents are still not understood with clarity. This review aims to comprehensively analyze the current state of research on plant-derived exosomes as a promising avenue for drug delivery, highlighting anticancer activity, challenges, and future orientation in effective cancer therapeutic development.

An Update on Prenatal Diethylstilbestrol Exposure and High-Grade Squamous Intraepithelial Lesions of the Lower Genital Tract.

Women with prenatal diethylstilbestrol exposure are excluded from less frequent cervical cancer screening because of their increased neoplasia risk. We report the results of a prospective follow-up study of prenatal diethylstilbestrol exposure and lower genital tract high-grade (grade 2 or higher) squamous intraepithelial lesions (HSIL). The age-adjusted risk of HSIL among diethylstilbestrol-exposed women (n=4,062) was higher than among the diethylstilbestrol unexposed (n=1,837) through age 44 years (hazard ratio 2.03, 95% CI, 1.31-3.14) but not age 45 years or older. Elevated HSIL risk remained higher in diethylstilbestrol-exposed women, after accounting for frequency of cervical cancer screening. Compared with unexposed women, HSIL risk was higher among women with earlier gestational and high-dose diethylstilbestrol exposure. These data confirm the appropriateness of more frequent screening among diethylstilbestrol-exposed women through age 44 years. Whether those aged 45 years or older should continue to have increased screening will require careful weighing of possible risks and benefits.

Unraveling the Mystery of Multi-Oocyte Follicles: An Observational Study.

Introduction: Significant progress has been made in understanding oocyte fertilization and early developmental stages through in vitro fertilization (IVF) techniques. However, irregularities such as conjoined oocytes and binucleate giant oocytes, which are exceptions to the normal rule of one diploid female gamete per follicle, can potentially lead to chromosomal disorders in embryos and are recommended to be excluded from IVF attempts. The formation of primordial follicles during ovarian development, known as follicle assembly, is a critical process that establishes the ovarian follicle reserve. Multi-oocyte follicles (MOFs) containing two or more oocytes have been observed in various species, including humans, and their clinical significance on fertility and reproductive health remains unclear. Genetic and environmental factors, such as gene knockout and exposure to endocrine disruptors, have been implicated in MOF formation, but the mechanisms are not fully understood and require further investigation. Material & Method: In this Observational study, 350 slides of ovarian tissues were scanned using an AI-based automated microscope, Grundium Ocus 20, and the TIFF images were stored in cloud storage. The slides were examined using third-party software, Pathcore Seeden Viewer, for morphometry of binovular follicles. Results: In our observational study, we examined 350 ovarian tissue slides in detail by using an AI-based microscope, uncovering 22 slides from seven different tissues with binovular and multinovular oocytes. These rare multi-oocyte follicles (MOFs) challenge the conventional one-oocyte-per-follicle paradigm. MOFs are likely formed when oocytes fail to separate during cortical sex cord proliferation, regulated by factors several molecular factorsas well as environmental factors. Discussion: Multiple Ovarian Follicles (MOFs) are rare phenomena where two or more oocytes exist in one follicle. They arise when oocytes fail to separate during ovarian development, which is governed by hormones such as follicle-stimulating hormone (FSH), inhibin, BMP-15, GDF-9, and GCNF. MOFs can be caused by dysregulation and exposure to chemicals such as diethylstilbestrol (DES) and isoflavones. Binovular oocytes, which occur when two oocytes are released during ovulation and are fertilised by different sperm, can result in non-identical twins, which are influenced by genetic and environmen-tal factors such as maternal age, heredity, hormonal imbalances, and assisted reproductive techniques such as in vitro fertilisation (IVF). Polynuclear oocytes with many nuclei can develop as a result of meiotic spindle defects and environmental influences. Identifying these oocyte types may aid in improving ART results by improving knowledge of the reasons of infertility and devising appropriate interventions.

The integration of multidisciplinary approaches revealed PTGES3 as a novel drug target for breast cancer treatment.

BACKGROUND: The main challenge in personalized treatment of breast cancer (BC) is how to integrate massive amounts of computing resources and data. This study aimed to identify a novel molecular target that might be effective for BC prognosis and for targeted therapy by using network-based multidisciplinary approaches. METHODS: Differentially expressed genes (DEGs) were first identified based on ESTIMATE analysis. A risk model in the TCGA-BRCA cohort was constructed using the risk score of six DEGs and validated in external and clinical in-house cohorts. Subsequently, independent prognostic factors in the internal and external cohorts were evaluated. Cell viability CCK-8 and wound healing assays were performed after PTGES3 siRNA was transiently transfected into the BC cell lines. Drug prediction and molecular docking between PTGES3 and drugs were further analyzed. Cell viability and PTGES3 expression in two BC cell lines after drug treatment were also investigated. RESULTS: A novel six-gene signature (including APOOL, BNIP3, F2RL2, HINT3, PTGES3 and RTN3) was used to establish a prognostic risk stratification model. The risk score was an independent prognostic factor that was more accurate than clinicopathological risk factors alone in predicting overall survival (OS) in BC patients. A high risk score favored tumor stage/grade but not OS. PTGES3 had the highest hazard ratio among the six genes in the signature, and its mRNA and protein levels significantly increased in BC cell lines. PTGES3 knockdown significantly inhibited BC cell proliferation and migration. Three drugs (gedunin, genistein and diethylstilbestrol) were confirmed to target PTGES3, and genistein and diethylstilbestrol demonstrated stronger binding affinities than did gedunin. Genistein and diethylstilbestrol significantly inhibited BC cell proliferation and reduced the protein and mRNA levels of PTGES3. CONCLUSIONS: PTGES3 was found to be a novel drug target in a robust six-gene prognostic signature that may serve as a potential therapeutic strategy for BC.

A physics-informed deep learning framework for modeling of coronary in-stent restenosis.

Machine learning (ML) techniques have shown great potential in cardiovascular surgery, including real-time stenosis recognition, detection of stented coronary anomalies, and prediction of in-stent restenosis (ISR). However, estimating neointima evolution poses challenges for ML models due to limitations in manual measurements, variations in image quality, low data availability, and the difficulty of acquiring biological quantities. An effective in silico model is necessary to accurately capture the mechanisms leading to neointimal hyperplasia. Physics-informed neural networks (PINNs), a novel deep learning (DL) method, have emerged as a promising approach that integrates physical laws and measurements into modeling. PINNs have demonstrated success in solving partial differential equations (PDEs) and have been applied in various biological systems. This paper aims to develop a robust multiphysics surrogate model for ISR estimation using the physics-informed DL approach, incorporating biological constraints and drug elution effects. The model seeks to enhance prediction accuracy, provide insights into disease progression factors, and promote ISR diagnosis and treatment planning. A set of coupled advection-reaction-diffusion type PDEs is constructed to track the evolution of the influential factors associated with ISR, such as platelet-derived growth factor (PDGF), the transforming growth factor- ß (TGF- ß ), the extracellular matrix (ECM), the density of smooth muscle cells (SMC), and the drug concentration. The nature of PINNs allows for the integration of patient-specific data (procedure-related, clinical and genetic, etc.) into the model, improving prediction accuracy and assisting in the optimization of stent implantation parameters to mitigate risks. This research addresses the existing gap in predictive models for ISR using DL and holds the potential to enhance patient outcomes through predictive risk assessment.

A combination of bowel wall thickness and submucosa index is useful for estimating endoscopic improvement in ulcerative colitis: external validation of the Kyorin Ultrasound Criterion.

BACKGROUND: Endoscopic improvement (EI; a Mayo endoscopic subscore of 0 or 1) is considered a therapeutic target in ulcerative colitis (UC) treatment. The potential to estimate EI non-invasively is an advantage of intestinal ultrasound (IUS). In a previous study, we developed a new sonographic parameter, the submucosa index (SMI), calculated as the ratio of the submucosal thickness to bowel wall thickness (BWT), and reported that combining BWT and SMI results in a practical and promising criterion for estimating EI without color Doppler assessment. This study aimed to validate the EI estimation ability of our B mode-based criterion, the 'Kyorin Ultrasound Criterion for UC' (KUC-UC; BWT < 3.8 mm and SMI < 50%), using an external cohort. METHODS: Patients with UC who underwent IUS and colonoscopy within 15 days without a treatment change between examinations were included. IUS findings, including BWT, SMI, and modified Limberg score for vascularity of the colon, were assessed. RESULTS: Forty-four test pairs of IUS and colonoscopy examinations in a total of 122 colonic segments were analyzed. The KUC-UC showed positive predictive value (PPV) of 94.6% and negative predictive value (NPV) of 80.0% for EI. In comparison, PPV and NPV were 85.4% and 79.0%, respectively, for the common criterion BWT of < 3 mm, and 83.0% and 82.7% for the validated Milan Ultrasound Criteria (a score of ≤ 6.2). CONCLUSIONS: External validation showed that the KUC-UC using only B mode findings without complicated calculations is a feasible and accurate sonographic criterion for estimating the EI of UC.

The perspective of cAMP/cGMP signaling and cyclic nucleotide phosphodiesterases in aortic aneurysm and dissection.

Aortic aneurysm (AA) and dissection (AD) are aortic diseases caused primarily by medial layer degeneration and perivascular inflammation. They are lethal when the rupture happens. Vascular smooth muscle cells (SMCs) play critical roles in the pathogenesis of medial degeneration, characterized by SMC loss and elastin fiber degradation. Many molecular pathways, including cyclic nucleotide signaling, have been reported in regulating vascular SMC functions, matrix remodeling, and vascular structure integrity. Intracellular cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP) are second messengers that mediate intracellular signaling transduction through activating effectors, such as protein kinase A (PKA) and PKG, respectively. cAMP and cGMP are synthesized by adenylyl cyclase (AC) and guanylyl cyclase (GC), respectively, and degraded by cyclic nucleotide phosphodiesterases (PDEs). In this review, we will discuss the roles and mechanisms of cAMP/cGMP signaling and PDEs in AA/AD formation and progression and the potential of PDE inhibitors in AA/AD, whether they are beneficial or detrimental. We also performed database analysis and summarized the results showing PDEs with significant expression changes under AA/AD, which should provide rationales for future research on PDEs in AA/AD.

Recurrent viral capture of cellular phosphodiesterases that antagonize OAS-RNase L.

Phosphodiesterases (PDEs) encoded by viruses are putatively acquired by horizontal transfer of cellular PDE ancestor genes. Viral PDEs inhibit the OAS-RNase L antiviral pathway, a key effector component of the innate immune response. Although the function of these proteins is well-characterized, the origins of these gene acquisitions are less clear. Phylogenetic analysis revealed at least five independent PDE acquisition events by ancestral viruses. We found evidence that PDE-encoding genes were horizontally transferred between coronaviruses belonging to different genera. Three clades of viruses within Nidovirales: merbecoviruses (MERS-CoV), embecoviruses (HCoV-OC43), and toroviruses encode independently acquired PDEs, and a clade of rodent alphacoronaviruses acquired an embecovirus PDE via recent horizontal transfer. Among rotaviruses, the PDE of rotavirus A was acquired independently from rotavirus B and G PDEs, which share a common ancestor. Conserved motif analysis suggests a link between all viral PDEs and a similar ancestor among the mammalian AKAP7 proteins despite low levels of sequence conservation. Additionally, we used ancestral sequence reconstruction and structural modeling to reveal that sequence and structural divergence are not well-correlated among these proteins. Specifically, merbecovirus PDEs are as structurally divergent from the ancestral protein and the solved structure of human AKAP7 PDE as they are from each other. In contrast, comparisons of rotavirus B and G PDEs reveal virtually unchanged structures despite evidence for loss of function in one, suggesting impactful changes that lie outside conserved catalytic sites. These findings highlight the complex and volatile evolutionary history of viral PDEs and provide a framework to facilitate future studies.

Physics-informed kernel function neural networks for solving partial differential equations.

This paper proposes an improved version of physics-informed neural networks (PINNs), the physics-informed kernel function neural networks (PIKFNNs), to solve various linear and some specific nonlinear partial differential equations (PDEs). It can also be considered as a novel radial basis function neural network (RBFNN). In the proposed PIKFNNs, it employs one-hidden-layer shallow neural network with the physics-informed kernel functions (PIKFs) as the customized activation functions. The PIKFs fully or partially contain PDE information, which can be chosen as fundamental solutions, green's functions, T-complete functions, harmonic functions, radial Trefftz functions, probability density functions and even the solutions of some linear simplified PDEs and so on. The main difference between the PINNs and the proposed PIKFNNs is that the PINNs add PDE constraints to the loss function, and the proposed PIKFNNs embed PDE information into the activation functions of the neural network. The feasibility and accuracy of the proposed PIKFNNs are validated by some benchmark examples.

Phosphodiesterase in heart and vessels: from physiology to diseases.

Phosphodiesterases (PDEs) are a superfamily of enzymes that hydrolyze cyclic nucleotides, including cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP). Both cyclic nucleotides are critical secondary messengers in the neurohormonal regulation in the cardiovascular system. PDEs precisely control spatiotemporal subcellular distribution of cyclic nucleotides in a cell- and tissue-specific manner, playing critical roles in physiological responses to hormone stimulation in the heart and vessels. Dysregulation of PDEs has been linked to the development of several cardiovascular diseases, such as hypertension, aneurysm, atherosclerosis, arrhythmia, and heart failure. Targeting these enzymes has been proven effective in treating cardiovascular diseases and is an attractive and promising strategy for the development of new drugs. In this review, we discuss the current understanding of the complex regulation of PDE isoforms in cardiovascular function, highlighting the divergent and even opposing roles of PDE isoforms in different pathogenesis.

Reduced Order Modeling for Real-Time Stent Deformation Simulations of Transcatheter Aortic Valve Prostheses.

Computational modeling can be a critical tool to predict deployment behavior for transcatheter aortic valve replacement (TAVR) in patients with aortic stenosis. However, due to the mechanical complexity of the aortic valve and the multiphysics nature of the problem, described by partial differential equations (PDEs), traditional finite element (FE) modeling of TAVR deployment is computationally expensive. In this preliminary study, a PDEs-based reduced order modeling (ROM) framework is introduced for rapidly simulating structural deformation of the Medtronic Evolut R valve stent frame. Using fifteen probing points from an Evolut model with parametrized loads enforced, 105 FE simulations were performed in the so-called offline phase, creating a snapshot library. The library was used in the online phase of the ROM for a new set of applied loads via the proper orthogonal decomposition-Galerkin (POD-Galerkin) approach. Simulations of small radial deformations of the Evolut stent frame were performed and compared to full order model (FOM) solutions. Linear elastic and hyperelastic constitutive models in steady and unsteady regimes were implemented within the ROM. Since the original POD-Galerkin method is formulated for linear problems, specific methods for the nonlinear terms in the hyperelastic case were employed, namely, the Discrete Empirical Interpolation Method. The ROM solutions were in strong agreement with the FOM in all numerical experiments, with a speed-up of at least 92% in CPU Time. This framework serves as a first step toward real-time predictive models for TAVR deployment simulations.

FlhF affects the subcellular clustering of WspR through HsbR in Pseudomonas aeruginosa.

In bacteria, the second messenger cyclic di-GMP (c-di-GMP) is synthesized and degraded by multiple diguanylate cyclases (DGCs) and phosphodiesterases. A high level of c-di-GMP induces biofilm formation and represses motility. WspR, a hybrid response regulator DGC, produces c-di-GMP when it is phosphorylated. FlhF, a signal recognition particle-type GTPase, is initially localized to the cell poles and is indispensable for polar flagellar localization in Pseudomonas aeruginosa. In this study, we report that deletion of flhF affected biofilm formation and the c-di-GMP level in P. aeruginosa. Phenotypic analysis of a flhF knockout mutant revealed increased biofilm formation, wrinkled colonies on Congo red agar, and an elevated c-di-GMP level compared to the wild-type strain, PAO1. Yeast and bacterial two-hybrid systems showed that FlhF binds to the response regulator HsbR, and HsbR binds to WspR. Deletion of hsbR or wspR in the ΔflhF background abolished the phenotype of ΔflhF. In addition, confocal microscopy demonstrated that WspR-GFP was distributed throughout the cytoplasm and formed a visible cluster at one cell pole in PAO1 and ΔhsbR, but it was mainly distributed as visible clusters at the lateral side of the periplasm and with visible clusters at both cell poles in ΔflhF. These findings suggest that FlhF influences the subcellular cluster and localization of WspR and negatively modulates WspR DGC activity in a manner dependent on HsbR. Together, our findings demonstrate a novel mechanism for FlhF modulating the lifestyle transition between motility and biofilm via HsbR to regulate the DGC activity of WspR.IMPORTANCECyclic di-GMP (c-di-GMP) is a second messenger that controls flagellum biosynthesis, adhesion, virulence, motility, exopolysaccharide production, and biofilm formation in bacteria. Recent research has shown that distinct diguanylate cyclases (DGCs) or phosphodiesterases (PDEs) produce highly specific outputs. Some DGCs and PDEs contribute to the total global c-di-GMP concentration, but others only affect local c-di-GMP in a microenvironment. However, the underlying mechanisms are unclear. Here, we report that FlhF affects the localization and DGC activity of WspR via HsbR and is implicated in local c-di-GMP signaling in Pseudomonas aeruginosa. This study establishes the link between the c-di-GMP signaling system and the flagellar localization and provides insight for understanding the complex regulatory network of c-di-GMP signaling.

The evolution of fentanyl-related substances: Prevalence and drug concentrations in postmortem biological specimens at the Miami-Dade Medical Examiner Department.

Since 2014, the Miami-Dade Medical Examiner Department (MDME) has observed a drastic increase in the number of fentanyl and fentanyl analog (fentanyl-related substances (FRSs)) fatalities since its introduction into the heroin and cocaine supply. Due to the prevalence of FRS in Miami-Dade County, the MDME toxicology laboratory began documenting each case in which fentanyl and/or a fentanyl analog was identified. Additional information monitored included demographics (age, race and sex), other drugs identified, cause of death (COD) and manner of death (MOD). From 2014 to 2022, the MDME toxicology laboratory analyzed a total of 1,989 cases that tested positive for FRS, of which 1,707 had detectable and/or quantifiable fentanyl concentrations in postmortem cases. The majority of decedents were white males (62%), and the predominant age range was 25-34 years. The most prevalent MOD was accident (93%) with the most common COD listed as acute combined drug toxicity of fentanyl in combination with other drugs (79%). Other drugs found in combination with fentanyl included heroin, cocaine (most prevalent), synthetic cathinones and ethanol. Of all FRS cases, 9% (170 cases) involved fentanyl alone as a COD, while 2% (38 cases) included only fentanyl analogs. Fentanyl concentrations ranged from 1.0 to 1,646 ng/mL in peripheral blood, 1.2 to 449 ng/mL in central blood, 3.2 to 28 ng/mL in donor blood (obtained during tissue harvesting), 1.1 to 108 ng/mL in antemortem blood, 8.5 to 1,130 ng/g in liver and 2.0 to 471 ng/g in brain. Drug concentrations were also reported for an additional eight fentanyl analogs. Considering the prevalence, high potency and constant evolution of FRS, it is important to continuously monitor trends and report drug concentrations in complex medical examiner casework in an effort to educate pathologists, law enforcement and local governments.