Transcriptomic analyses of bacterial growth on fungal necromass reveal different microbial community niches during degradation.

Bacteria are major drivers of organic matter decomposition and play crucial roles in global nutrient cycling. Although the degradation of dead fungal biomass (necromass) is increasingly recognized as an important contributor to soil carbon (C) and nitrogen (N) cycling, the genes and metabolic pathways involved in necromass degradation are less characterized. In particular, how bacteria degrade necromass containing different quantities of melanin, which largely control rates of necromass decomposition in situ, is largely unknown. To address this gap, we conducted a multi-timepoint transcriptomic analysis using three Gram-negative, bacterial species grown on low or high melanin necromass of Hyaloscypha bicolor. The bacterial species, Cellvibrio japonicus, Chitinophaga pinensis, and Serratia marcescens, belong to genera known to degrade necromass in situ. We found that while bacterial growth was consistently higher on low than high melanin necromass, the CAZyme-encoding gene expression response of the three species was similar between the two necromass types. Interestingly, this trend was not shared for genes encoding nitrogen utilization, which varied in C. pinensis and S. marcescens during growth on high vs low melanin necromass. Additionally, this study tested the metabolic capabilities of these bacterial species to grow on a diversity of C and N sources and found that the three bacteria have substantially different utilization patterns. Collectively, our data suggest that as necromass changes chemically over the course of degradation, certain bacterial species are favored based on their differential metabolic capacities.IMPORTANCEFungal necromass is a major component of the carbon (C) in soils as well as an important source of nitrogen (N) for plant and microbial growth. Bacteria associated with necromass represent a distinct subset of the soil microbiome and characterizing their functional capacities is the critical next step toward understanding how they influence necromass turnover. This is particularly important for necromass varying in melanin content, which has been observed to control the rate of necromass decomposition across a variety of ecosystems. Here we assessed the gene expression of three necromass-degrading bacteria grown on low or high melanin necromass and characterized their metabolic capacities to grow on different C and N substrates. These transcriptomic and metabolic studies provide the first steps toward assessing the physiological relevance of up-regulated CAZyme-encoding genes in necromass decomposition and provide foundational data for generating a predictive model of the molecular mechanisms underpinning necromass decomposition by soil bacteria.

Feedstock variability impacts the bioconversion of sugar and lignin streams derived from corn stover by Clostridium tyrobutyricum and engineered Pseudomonas putida.

Feedstock variability represents a challenge in lignocellulosic biorefineries, as it can influence both lignocellulose deconstruction and microbial conversion processes for biofuels and biochemicals production. The impact of feedstock variability on microbial performance remains underexplored, and predictive tools for microbial behaviour are needed to mitigate risks in biorefinery scale-up. Here, twelve batches of corn stover were deconstructed via deacetylation, mechanical refining, and enzymatic hydrolysis to generate lignin-rich and sugar streams. These batches and their derived streams were characterised to identify their chemical components, and the streams were used as substrates for producing muconate and butyrate by engineered Pseudomonas putida and wildtype Clostridium tyrobutyricum, respectively. Bacterial performance (growth, product titers, yields, and productivities) differed among the batches, but no strong correlations were identified between feedstock composition and performance. To provide metabolic insights into the origin of these differences, we evaluated the effect of twenty-three isolated chemical components on these microbes, including three components in relevant bioprocess settings in bioreactors, and we found that growth-inhibitory concentrations were outside the ranges observed in the streams. Overall, this study generates a foundational dataset on P. putida and C. tyrobutyricum performance to enable future predictive models and underscores their resilience in effectively converting fluctuating lignocellulose-derived streams into bioproducts.

Subject-level spinal osteoporotic fracture prediction combining deep learning vertebral outputs and limited demographic data.

Automated screening for vertebral fractures could improve outcomes. We achieved an AUC-ROC = 0.968 for the prediction of moderate to severe fracture using a GAM with age and three maximal vertebral body scores of fracture from a convolutional neural network. Maximal fracture scores resulted in a performant model for subject-level fracture prediction. Combining individual deep learning vertebral body fracture scores and demographic covariates for subject-level classification of osteoporotic fracture achieved excellent performance (AUC-ROC of 0.968) on a large dataset of radiographs with basic demographic data. PURPOSE: Osteoporotic vertebral fractures are common and morbid. Automated opportunistic screening for incidental vertebral fractures from radiographs, the highest volume imaging modality, could improve osteoporosis detection and management. We consider how to form patient-level fracture predictions and summarization to guide management, using our previously developed vertebral fracture classifier on segmented radiographs from a prospective cohort study of US men (MrOS). We compare the performance of logistic regression (LR) and generalized additive models (GAM) with combinations of individual vertebral scores and basic demographic covariates. METHODS: Subject-level LR and GAM models were created retrospectively using all fracture predictions or summary variables such as order statistics, adjacent vertebral interactions, and demographic covariates (age, race/ethnicity). The classifier outputs for 8663 vertebrae from 1176 thoracic and lumbar radiographs in 669 subjects were divided by subject to perform stratified fivefold cross-validation. Models were assessed using multiple metrics, including receiver operating characteristic (ROC) and precision-recall (PR) curves. RESULTS: The best model (AUC-ROC = 0.968) was a GAM using the top three maximum vertebral fracture scores and age. Using top-ranked scores only, rather than all vertebral scores, improved performance for both model classes. Adding age, but not ethnicity, to the GAMs improved performance slightly. CONCLUSION: Maximal vertebral fracture scores resulted in the highest-performing models. While combining multiple vertebral body predictions risks decreasing specificity, our results demonstrate that subject-level models maintain good predictive performance. Thresholding strategies can be used to control sensitivity and specificity as clinically appropriate.

Plasma concentrations of multiple oxysterols and risk of colorectal adenomas.

Oxysterols are metabolites of cholesterol that regulate homeostasis of cholesterol, fatty acids, and glucose. These metabolites are generated throughout the body, either enzymatically or from oxidative stress, and are detectable in peripheral circulation. We previously reported that circulating 27-hydroxycholesterol (27-OHC), an endogenous selective estrogen receptor modulator, may be a risk factor for colorectal adenomas. Here, in addition to 27-OHC, we report on four other circulating oxysterols: 25-hydroxycholesterol (25-OHC), 24(S)-hydroxycholesterol (24(S)-OHC), 7ɑ-hydroxycholesterol (7ɑ-OHC), and 4ß-hydroxycholesterol (4ß-OHC). Oxysterol concentrations were measured using liquid chromatography-mass spectrometry from fasting plasma collected at baseline from 1,246 participants of the Vitamin D/Calcium Polyp Prevention Study, a multicenter adenoma chemoprevention trial. To evaluate multiple oxysterols simultaneously, we used both log-linear regression and Bayesian kernel machine regression (BKMR) models developed for analyses of complex mixtures adjusted for potential confounding factors. Higher circulating 7ɑ-OHC was associated with higher adenoma risk (BKMR-based multivariable-adjusted risk ratios, RR, for the 75th vs. 25th percentile, 1.22; 95% credible interval, CI, 1.04-1.42). In contrast, higher circulating 4ß-OHC was associated with lower risk of these polyps (RR, 0.84; 95% CI, 0.71-0.99). The positive association with advanced adenoma risk that we previously reported for circulating 27-OHC persisted when controlling for other oxysterols (RR, 1.26; 95% CI, 0.98-1.62), including among those with advanced adenomas at baseline (RR, 1.75; 95% CI, 1.01-3.06).

Development and Evaluation of a Real-Time Phase-Triggered Stimulation Algorithm for the CorTec Brain Interchange.

With the development and characterization of biomarkers that may reflect neural network state as well as a patient's clinical deficits, there is growing interest in more complex stimulation designs. While current implantable neuromodulation systems offer pathways to expand the design and application of adaptive stimulation paradigms, technological drawbacks of these systems limit adaptive neuromodulation exploration. In this paper, we discuss the implementation of a phase-triggered stimulation paradigm using a research platform composed of an investigational system known as the CorTec Brain Interchange (CorTec GmbH, Freiburg, Germany), and an open-source software tool known as OMNI-BIC. We then evaluate the stimulation paradigm's performance in both benchtop and in vivo human demonstrations. Our findings indicate that the Brain Interchange and OMNI-BIC platform is capable of reliable administration of phase-triggered stimulation and has the potential to help expand investigation within the adaptive neuromodulation design space.

Designing thermoreversible gels for extended release of mosquito repellent.

Mosquito-borne diseases are responsible for 700 000 deaths annually. Current outdoor protective strategies primarily focus on direct skin application of commercial repellents (i.e., aerosol sprays or topical lotions) which are typically limited to efficacy times of ≤10 hours due to rapid evaporation and dermal absorption. Consequently, frequent reapplication for continuous protection can increase associated health hazards and cause noncompliance. This study utilizes Hansen solubility parameter modeling to design physical gels composed of insect-repelling N,N-diethyl-meta-toluamide (DEET) and modacrylic copolymer poly(acrylonitrile-co-vinyl chloride) (P(AN-VC)). The P(AN-VC)/DEET composites exhibit tunable and reversible sol-gel transition temperatures that can meet the thermomechanical stability demands of the intended application and permit facile transition to commercial melt processing techniques such as injection molding, filament spinning, or film casting. P(AN-VC)/DEET gel films demonstrate mosquito repellency for more than half a year-performing longer than any other known material to date-due to the high reservoir of repellent and its desorption hindrance from the polymer matrix. Therefore, P(AN-VC)/DEET gels hold significant potential for extended protection against mosquitos and other biting arthropods.

The Lipidomics Reporting Checklist A framework for transparency of lipidomic experiments and repurposing resource data.

The rapid increase in lipidomic studies has led to a collaborative effort within the community to establish standards and criteria for producing, documenting, and disseminating data. Creating a dynamic easy-to-use checklist that condenses key information about lipidomic experiments into common terminology will enhance the field's consistency, comparability, and repeatability. Here, we describe the structure and rationale of the established Lipidomics Minimal Reporting Checklist to increase transparency in lipidomics research.

Genetic risk factors for COVID-19 and influenza are largely distinct.

Coronavirus disease 2019 (COVID-19) and influenza are respiratory illnesses caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses, respectively. Both diseases share symptoms and clinical risk factors1, but the extent to which these conditions have a common genetic etiology is unknown. This is partly because host genetic risk factors are well characterized for COVID-19 but not for influenza, with the largest published genome-wide association studies for these conditions including >2 million individuals2 and about 1,000 individuals3-6, respectively. Shared genetic risk factors could point to targets to prevent or treat both infections. Through a genetic study of 18,334 cases with a positive test for influenza and 276,295 controls, we show that published COVID-19 risk variants are not associated with influenza. Furthermore, we discovered and replicated an association between influenza infection and noncoding variants in B3GALT5 and ST6GAL1, neither of which was associated with COVID-19. In vitro small interfering RNA knockdown of ST6GAL1-an enzyme that adds sialic acid to the cell surface, which is used for viral entry-reduced influenza infectivity by 57%. These results mirror the observation that variants that downregulate ACE2, the SARS-CoV-2 receptor, protect against COVID-19 (ref. 7). Collectively, these findings highlight downregulation of key cell surface receptors used for viral entry as treatment opportunities to prevent COVID-19 and influenza.

Molecular insights into human phosphatidylserine synthase 1 reveal its inhibition promotes LDL uptake.

In mammalian cells, two phosphatidylserine (PS) synthases drive PS synthesis. Gain-of-function mutations in the Ptdss1 gene lead to heightened PS production, causing Lenz-Majewski syndrome (LMS). Recently, pharmacological inhibition of PSS1 has been shown to suppress tumorigenesis. Here, we report the cryo-EM structures of wild-type human PSS1 (PSS1WT), the LMS-causing Pro269Ser mutant (PSS1P269S), and PSS1WT in complex with its inhibitor DS55980254. PSS1 contains 10 transmembrane helices (TMs), with TMs 4-8 forming a catalytic core in the luminal leaflet. These structures revealed a working mechanism of PSS1 akin to the postulated mechanisms of the membrane-bound O-acyltransferase family. Additionally, we showed that both PS and DS55980254 can allosterically inhibit PSS1 and that inhibition by DS55980254 activates the SREBP pathways, thus enhancing the expression of LDL receptors and increasing cellular LDL uptake. This work uncovers a mechanism of mammalian PS synthesis and suggests that selective PSS1 inhibitors have the potential to lower blood cholesterol levels.

Decoding micro-electrocorticographic signals by using explainable 3D convolutional neural network to predict finger movements.

BACKGROUND: Electroencephalography (EEG) and electrocorticography (ECoG) recordings have been used to decode finger movements by analyzing brain activity. Traditional methods focused on single bandpass power changes for movement decoding, utilizing machine learning models requiring manual feature extraction. NEW METHOD: This study introduces a 3D convolutional neural network (3D-CNN) model to decode finger movements using ECoG data. The model employs adaptive, explainable AI (xAI) techniques to interpret the physiological relevance of brain signals. ECoG signals from epilepsy patients during awake craniotomy were processed to extract power spectral density across multiple frequency bands. These data formed a 3D matrix used to train the 3D-CNN to predict finger trajectories. RESULTS: The 3D-CNN model showed significant accuracy in predicting finger movements, with root-mean-square error (RMSE) values of 0.26-0.38 for single finger movements and 0.20-0.24 for combined movements. Explainable AI techniques, Grad-CAM and SHAP, identified the high gamma (HG) band as crucial for movement prediction, showing specific cortical regions involved in different finger movements. These findings highlighted the physiological significance of the HG band in motor control. COMPARISON WITH EXISTING METHODS: The 3D-CNN model outperformed traditional machine learning approaches by effectively capturing spatial and temporal patterns in ECoG data. The use of xAI techniques provided clearer insights into the model's decision-making process, unlike the "black box" nature of standard deep learning models. CONCLUSIONS: The proposed 3D-CNN model, combined with xAI methods, enhances the decoding accuracy of finger movements from ECoG data. This approach offers a more efficient and interpretable solution for brain-computer interface (BCI) applications, emphasizing the HG band's role in motor control.

Using an Ensemble of Segmentation Methods to Detect Vertebral Bodies on Radiographs.

BACKGROUND AND PURPOSE: Vertebral compression fractures may indicate osteoporosis but are underdiagnosed and underreported by radiologists. We have developed an ensemble of vertebral body (VB) segmentation models for lateral radiographs as a critical component of an automated, opportunistic screening tool. Our goal is to detect the approximate location of thoracic and lumbar VBs, including fractured vertebra, on lateral radiographs. MATERIALS AND METHODS: The Osteoporotic Fractures in Men Study (MrOS) data set includes spine radiographs of 5994 men aged ≥65 years from 6 clinical centers. Two segmentation models, U-Net and Mask-RCNN (Region-based Convolutional Neural Network), were independently trained on the MrOS data set retrospectively, and an ensemble was created by combining them. Primary performance metrics for VB detection success included precision, recall, and F1 score for object detection on a held-out test set. Intersection over union (IoU) and Dice coefficient were also calculated as secondary metrics of performance for the test set. A separate external data set from a quaternary health care enterprise was acquired to test generalizability, comprising diagnostic clinical radiographs from men and women aged ≥65 years. RESULTS: The trained models achieved F1 score of U-Net = 83.42%, Mask-RCNN = 86.30%, and ensemble = 88.34% in detecting all VBs, and F1 score of U-Net = 87.88%, Mask-RCNN = 92.31%, and ensemble = 97.14% in detecting severely fractured vertebrae. The trained models achieved an average IoU per VB of 0.759 for U-Net and 0.709 for Mask-RCNN. The trained models achieved F1 score of U-Net = 81.11%, Mask-RCNN = 79.24%, and ensemble = 87.72% in detecting all VBs in the external data set. CONCLUSIONS: An ensemble model combining predictions from U-Net and Mask-RCNN resulted in the best performance in detecting VBs on lateral radiographs and generalized well to an external data set. This model could be a key component of a pipeline to detect fractures on all vertebrae in a radiograph in an automated, opportunistic screening tool under development.

Planned Dental Extractions After Radiation Therapy.

Importance: Nonrestorable teeth are recommended to be extracted prior to radiation therapy (RT). Occasionally, preradiation extractions introduce unacceptable delays in treatment initiation. Planned dental extractions immediately postradiation presents an alternative strategy, though outcomes are uncertain. Objective: To evaluate the feasibility and safety of dental extractions immediately postradiation. Design, Setting, and Participants: A prospective cohort study including patients planned for curative-intent RT but unable or unwilling to proceed with 1 or more extractions recommended pretreatment was carried out. From January 2020 to September 2022, 58 patients were screened and 50 enrolled. The dental care was performed at a single academic department and the cancer care at regional centers. Analysis took place between September 22, 2023, and June 10, 2024. Exposure: On completion of RT, patients were recommended to complete extractions as soon as feasible, and ideally within 4 months. Main Outcomes and Measures: The primary end point was the actuarial cumulative incidence of exposed alveolar bone noted by any practitioner at any time after extraction, calculated using Gray method with death as a competing risk. As a pilot study, no formal power calculation was performed; resources allowed for 50 evaluable patients. Results: Among the 50 participants enrolled, RT was nonoperative for 32 patients (64%) and postoperative for 18 patients (36%). Intensity-modulated RT (IMRT) was delivered in all patients. Of the 50 patients, 20 (40%) declined dental extractions immediately postradiation and the remaining 30 (60%) underwent a median (range) of 8.5 (1-28) extractions at a median (range) of 64.5 (13-152) days after RT. The median (IQR) follow-up for survivors without exposed bone was 26 (17-35) months from the end of RT. The 2-year cumulative incidence of any exposed bone was 27% (95% CI, 14%-40%). The 2-year incidence of exposed bone for those who underwent dental extractions immediately postradiation was 40% (95% CI, 22%-58%) and 7% (95% CI, 0%-22%) for those who did not. Of the 13 who developed exposed bone: 4 resolved, 1 was lost to follow-up, and 8 were confirmed as osteoradionecrosis. Conclusions and Relevance: This cohort study found that postradiation dental extractions incur considerable risk, even if performed within a 4-month window.

Integration of 3D printed Mg2+ potentiometric sensors into microfluidic devices for bioanalysis.

Electrochemical sensors provide an affordable and reliable approach towards the detection and monitoring of important biological species ranging from simple ions to complex biomolecules. The ability to miniaturize electrochemical sensors, coupled with their affordability and simple equipment requirements for signal readout, permits the use of these sensors at the point-of-care where analysis using non-invasively obtainable biofluids is receiving growing interest by the research community. This paper describes the design, fabrication, and integration of a 3D printed Mg2+ potentiometric sensor into a 3D printed microfluidic device for the quantification of Mg2+ in low-sample volume biological fluids. The sensor employs a functionalized 3D printable photocurable methacrylate-based ion-selective membrane affixed to a carbon-mesh/epoxy solid-contact transducer for the selective determination of Mg2+ in sweat, saliva and urine. The 3D printed Mg2+ ion-selective electrode (3Dp-Mg2+-ISE) provided a Nernstian response of 27.5 mV per decade with a linear range of 10 mM to 39 µM, covering the normal physiological and clinically relevant levels of Mg2+ in biofluids. 3Dp-Mg2+-ISEs selectively measure Mg2+ over other biologically present cations - sodium, potassium, calcium, ammonium - as well as provide high stability in the analytical signal with a drift of just 13 µV h-1 over 10 hours. Comparison with poly(vinylchloride)-based Mg2+-ISEs showed distinct advantages to the use of 3Dp-Mg2+-ISEs, with respect to stability, resilience towards biofouling and importantly providing a streamlined and rapid approach towards mass production of selective and reliable sensors. The miniaturization capabilities of 3D printing coupled with the benefits of microfluidic analysis (i.e., low sample volumes, minimal reagent consumption, automation of multiple assays, etc.), provides exciting opportunities for the realization of the next-generation of point-of-care diagnostic devices.

Elevated Inflammation Associated with Markers of Neutrophil Function and Gastrointestinal Disruption in Pilot Study of Plasmodium fragile Co-Infection of ART-Treated SIVmac239+ Rhesus Macaques.

Human immunodeficiency virus (HIV) and malaria, caused by infection with Plasmodium spp., are endemic in similar geographical locations. As a result, there is high potential for HIV/Plasmodium co-infection, which increases the pathology of both diseases. However, the immunological mechanisms underlying the exacerbated disease pathology observed in co-infected individuals are poorly understood. Moreover, there is limited data available on the impact of Plasmodium co-infection on antiretroviral (ART)-treated HIV infection. Here, we used the rhesus macaque (RM) model to conduct a pilot study to establish a model of Plasmodium fragile co-infection during ART-treated simian immunodeficiency virus (SIV) infection, and to begin to characterize the immunopathogenic effect of co-infection in the context of ART. We observed that P. fragile co-infection resulted in parasitemia and anemia, as well as persistently detectable viral loads (VLs) and decreased absolute CD4+ T-cell counts despite daily ART treatment. Notably, P. fragile co-infection was associated with increased levels of inflammatory cytokines, including monocyte chemoattractant protein 1 (MCP-1). P. fragile co-infection was also associated with increased levels of neutrophil elastase, a plasma marker of neutrophil extracellular trap (NET) formation, but significant decreases in markers of neutrophil degranulation, potentially indicating a shift in the neutrophil functionality during co-infection. Finally, we characterized the levels of plasma markers of gastrointestinal (GI) barrier permeability and microbial translocation and observed significant correlations between indicators of GI dysfunction, clinical markers of SIV and Plasmodium infection, and neutrophil frequency and function. Taken together, these pilot data verify the utility of using the RM model to examine ART-treated SIV/P. fragile co-infection, and indicate that neutrophil-driven inflammation and GI dysfunction may underlie heightened SIV/P. fragile co-infection pathogenesis.

The emerging role of rapid corticosteroid actions on excitatory and inhibitory synaptic signaling in the brain.

Over the past two decades, there has been increasing evidence for the importance of rapid-onset actions of corticosteroid hormones in the brain. Here, we highlight the distinct rapid corticosteroid actions that regulate excitatory and inhibitory synaptic transmission in the hypothalamus, the hippocampus, basolateral amygdala, and prefrontal cortex. The receptors that mediate rapid corticosteroid actions are located at or close to the plasma membrane, though many of the receptor characteristics remain unresolved. Rapid-onset corticosteroid effects play a role in fast neuroendocrine feedback as well as in higher brain functions, including increased aggression and anxiety, and impaired memory retrieval. The rapid non-genomic corticosteroid actions precede and complement slow-onset, long-lasting transcriptional actions of the steroids. Both rapid and slow corticosteroid actions appear to be indispensable to adapt to a continuously changing environment, and their imbalance can increase an individual's susceptibility to psychopathology.

The Surgical Infection Society Guidelines on the Management of Intra-Abdominal Infection: 2024 Update.

Background: The Surgical Infection Society (SIS) published evidence-based guidelines for the management of intra-abdominal infection (IAI) in 1992, 2002, 2010, and 2017. Here, we present the most recent guideline update based on a systematic review of current literature. Methods: The writing group, including current and former members of the SIS Therapeutics and Guidelines Committee and other individuals with content or guideline expertise within the SIS, working with a professional librarian, performed a systematic review using PubMed/Medline, the Cochrane Library, Embase, and Web of Science from 2016 until February 2024. Keyword descriptors combined "surgical site infections" or "intra-abdominal infections" in adults limited to randomized controlled trials, systematic reviews, and meta-analyses. Additional relevant publications not in the initial search but identified during literature review were included. The Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) system was utilized to evaluate the evidence. The strength of each recommendation was rated strong (1) or weak (2). The quality of the evidence was rated high (A), moderate (B), or weak (C). The guideline contains new recommendations and updates to recommendations from previous IAI guideline versions. Final recommendations were developed by an iterative process. All writing group members voted to accept or reject each recommendation. Results: This updated evidence-based guideline contains recommendations from the SIS for the treatment of adult patients with IAI. Evidence-based recommendations were developed for antimicrobial agent selection, timing, route of administration, duration, and de-escalation; timing of source control; treatment of specific pathogens; treatment of specific intra-abdominal disease processes; and implementation of hospital-based antimicrobial agent stewardship programs. Summary: This document contains the most up-to-date recommendations from the SIS on the prevention and management of IAI in adult patients.

Female sex hormones exacerbate retinal neurodegeneration.

Neurodegenerative disorders such as Alzheimer's disease and macular degeneration represent major sources of human suffering, yet the factors influencing disease severity remain poorly understood. Sex has been implicated as one potential modifying factor. Here, we show that female sex is a risk factor for worsened outcomes in a model of retinal degeneration. Further, we show that this susceptibility is caused by the presence of female-specific circulating sex hormones. The adverse effect of female sex hormones was specific to diseased retinal neurons, and depletion of these hormones ameliorated this phenotypic effect. These findings provide novel insights into the pathogenesis of neurogenerative diseases and how sex hormones can impact the severity of disease. These findings have far-reaching implications for clinical trial design and the use of hormonal therapy in females with certain neurogenerative disorders.

Neuromodulation of inhibitory synaptic transmission in the basolateral amygdala during fear and anxiety.

The basolateral amygdala plays pivotal roles in the regulation of fear and anxiety and these processes are profoundly modulated by different neuromodulatory systems that are recruited during emotional arousal. Recent studies suggest activities of BLA interneurons and inhibitory synaptic transmission in BLA principal cells are regulated by neuromodulators to influence the output and oscillatory network states of the BLA, and ultimately the behavioral expression of fear and anxiety. In this review, we first summarize a cellular mechanism of stress-induced anxiogenesis mediated by the interaction of glucocorticoid and endocannabinoid signaling at inhibitory synapses in the BLA. Then we discuss cell type-specific activity patterns induced by neuromodulators converging on the Gq signaling pathway in BLA perisomatic parvalbumin-expressing (PV) and cholecystokinin-expressing (CCK) basket cells and their effects on BLA network oscillations and fear learning.