Dynamic assemblies of parvalbumin interneurons in brain oscillations.

Brain oscillations are crucial for perception, memory, and behavior. Parvalbumin-expressing (PV) interneurons are critical for these oscillations, but their population dynamics remain unclear. Using voltage imaging, we simultaneously recorded membrane potentials in up to 26 PV interneurons in vivo during hippocampal ripple oscillations in mice. We found that PV cells generate ripple-frequency rhythms by forming highly dynamic cell assemblies. These assemblies exhibit rapid and significant changes from cycle to cycle, varying greatly in both size and membership. Importantly, this variability is not just random spiking failures of individual neurons. Rather, the activities of other PV cells contain significant information about whether a PV cell spikes or not in a given cycle. This coordination persists without network oscillations, and it exists in subthreshold potentials even when the cells are not spiking. Dynamic assemblies of interneurons may provide a new mechanism to modulate postsynaptic dynamics and impact cognitive functions flexibly and rapidly.

Drug-drug interaction and initial dosage optimization of aripiprazole in patients with schizophrenia based on population pharmacokinetics.

Background: The present study aimed to investigate the drug-drug interaction and initial dosage optimization of aripiprazole in patients with schizophrenia based on population pharmacokinetics. Research design and methods: A total of 119 patients with schizophrenia treated with aripiprazole were included to build an aripiprazole population pharmacokinetic model using nonlinear mixed effects. Results: The weight and concomitant medication of fluoxetine influenced aripiprazole clearance. Under the same weight, the aripiprazole clearance rates were 0.714:1 in patients with or without fluoxetine, respectively. In addition, without fluoxetine, for the once-daily aripiprazole regimen, dosages of 0.3 and 0.2 mg kg-1 day-1 were recommended for patients with schizophrenia weighing 40-95 and 95-120 kg, respectively, while for the twice-daily aripiprazole regimen, 0.3 mg kg-1 day-1 was recommended for those weighing 40-120 kg. With fluoxetine, for the once-daily aripiprazole regimen, a dosage of 0.2 mg kg-1 day-1 was recommended for patients with schizophrenia weighing 40-120 kg, while for the twice-daily aripiprazole regimen, 0.3 and 0.2 mg kg-1 day-1 were recommended for those weighing 40-60 and 60-120 kg, respectively. Conclusion: This is the first investigation of the effects of fluoxetine on aripiprazole via drug-drug interaction. The optimal aripiprazole initial dosage is recommended in patients with schizophrenia.

Conversion of Propargylic Amines with CO2 Catalyzed by a Highly Stable Copper(I) Iodide Thorium-Based Heterometal-Organic Framework.

The conversion of CO2 to generate high-value-added chemicals has become one of the hot research topics in green synthesis. Thereinto, the cyclization reaction of propargylic amines with CO2 is highly attractive because the resultant oxazolidinones are widely found in pharmaceutical chemistry. Cu(I)-based metal-organic frameworks (MOFs) as catalysts exhibit promising application prospects for CO2 conversion. However, their practical application was greatly limited due to Cu(I) being liable to disproportionation or oxidization. Herein, the solid copper(I) iodide thorium-based porous framework {[Cu5I6Th6(µ3-O)4(µ3-OH)4(H2O)10(L)10]·OH·4DMF·H2O}n (1) (HL = 2-methylpyridine-4-carboxylic acid) constructed by [Th6] clusters and [CuxIy] subunits was successfully prepared and structurally characterized. To our knowledge, this is the first copper(I) iodide-based actinide organic framework. Catalytic investigations indicate that 1 can effectively catalyze the cyclization of propargylic amines with CO2 under ambient conditions, which can be reused at least five times without a remarkable decline of catalytic activity. Importantly, 1 exhibits excellent chemical stability and the oxidation state of Cu(I) in it can remain stable under various conditions. This work can provide a valuable strategy for the synthesis of stable Cu(I)-MOF materials.

Oriented Antibody Coupling to an Antifouling Polymer Using Glycan Remodeling for Biosensing by Particle Motion.

Biosensors based on immobilized antibodies require molecular strategies that (i) couple the antibodies in a stable fashion while maintaining the conformation and functionality, (ii) give outward orientation of the paratope regions of the antibodies for good accessibility to analyte molecules in the biofluid, and (iii) surround the antibodies by antibiofouling molecules. Here, we demonstrate a method to achieve oriented coupling of antibodies to an antifouling poly(l-lysine)-grafted-poly(ethylene glycol) (PLL-g-PEG) substrate, using glycan remodeling to create antibody-DNA conjugates. The coupling, orientation, and functionality of the antibodies were studied using two analysis methods with single-molecule resolution, namely single-molecule localization microscopy and continuous biosensing by particle motion. The biosensing functionality of the glycan-remodeled antibodies was demonstrated in a sandwich immunosensor for procalcitonin. The results show that glycan-remodeled antibodies enable oriented immobilization and biosensing functionality with low nonspecific binding on antifouling polymer substrates.

Personalized prediction of intradialytic hypotension in clinical practice: Development and evaluation of a novel AI dashboard incorporating risk factors from previous and current dialysis sessions.

BACKGROUND: Intradialytic hypotension (IDH) is one of the most common and critical complications of hemodialysis. Despite many proven factors associated with IDH, accurately predicting it before it occurs for individual patients during dialysis sessions remains a challenge. PURPOSE: To establish artificial intelligence (AI) predictive models for IDH, which consider risk factors from previous and ongoing dialysis to optimize model performance. We then implement a novel digital dashboard with the best model for continuous monitoring of patients' status undergoing hemodialysis. The AI dashboard can display the real-time probability of IDH for each patient in the hemodialysis center providing an objective reference for care members for monitoring IDH and treating it in advance. METHODS: Eight machine learning (ML) algorithms, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Light Gradient Boosting Machine (LightGBM), Multilayer Perception (MLP), eXtreme Gradient Boosting (XGBoost), and NaiveBayes, were used to establish the predictive model of IDH to determine if the patient will acquire IDH within 60 min. In addition to real-time features, we incorporated several features sourced from previous dialysis sessions to improve the model's performance. The electronic medical records of patients who had undergone hemodialysis at Chi Mei Medical Center between September 1, 2020 and December 31, 2020 were included in this research. Impact evaluation of AI assistance was conducted by IDH rate. RESULTS: The results showed that the XGBoost model had the best performance (accuracy: 0.858, sensitivity: 0.858, specificity: 0.858, area under the curve: 0.936) and was chosen for AI dashboard implementation. The care members were delighted with the dashboard providing real-time scientific probabilities for IDH risk and historic predictive records in a graphic style. Other valuable functions were appended in the dashboard as well. Impact evaluation indicated a significant decrease in IDH rate after the application of AI assistance. CONCLUSION: This AI dashboard provides high-quality results in IDH risk prediction during hemodialysis. High-risk patients for IDH will be recognized 60 min earlier, promoting individualized preventive interventions as part of the treatment plan. Our approachis believed to promise an excellent way for IDH management.

Relationship between plasma TNF-α levels and agitation symptoms in first episode patients with schizophrenia.

BACKGROUND: Increasing evidence suggested that immune abnormalities involved in the pathophysiology of schizophrenia. However, the relationship between immunity and clinical features has not been clarified. The aim of this study was to measure the plasma levels of tumor necrosis factor alpha (TNF-α) and soluble TNF-α receptor 1 (sTNF-α R1) and to investigate their association with agitation in first episode patients with schizophrenia (FEPS). METHODS: The plasma TNF-α and sTNF-α R1 levels were measured using sandwich enzyme-linked immunosorbent assay (ELISA) in the FEPS with (n = 36) and without agitation (n = 49) symptoms, and healthy controls (HCs, n = 54). The psychopathology was assessed by the Positive and Negative Syndrome Scale (PANSS), and the agitation symptoms were evaluated by the PANSS excitatory component (PANSS-EC). RESULTS: The plasma TNF-α levels in patients with and without agitation symptoms were significantly higher than those in HCs. The patients with agitation had significantly higher plasma TNF-α levels compared to the patients without agitation. There were no significant differences in the sTNF-α R1 levels among the three groups. Furthermore, the plasma TNF-α levels were positively correlated with the PANSS total score, Positive and General psychopathological subscores, and PANSS-EC score in the FEPS, but the relationships were not found for the plasma sTNF-α R1 levels. CONCLUSIONS: These results suggested that TNF-α might play an important role in the onset and development of agitation symptoms of schizophrenia.

Analysis of hemorrhagic drug-drug interactions between P-gp inhibitors and direct oral anticoagulants from the FDA Adverse Event Reporting System.

BACKGROUND: Limited understanding exists regarding the hemorrhagic risk resulting from potential interactions between P-glycoprotein (P-gp) inhibitors and direct oral anticoagulants (DOACs). Utilizing the Food and Drug Administration Adverse Event Reporting System (FAERS) data, we analyzed hemorrhagic adverse events (AEs) linked with the co-administration of P-gp inhibitors and DOACs, aiming to offer guidance for their safe and rational use. METHODS: Hemorrhagic events associated with P-gp inhibitors in combination with DOACs were scrutinized from the FAERS database. Hemorrhagic signals mining was performed by estimating the reported odds ratios (RORs), corroborated by additive and multiplicative models and a combination risk ratio (PRR) model. RESULTS: Our analysis covered 4,417,195 cases, revealing 11,967 bleeding events associated with P-gp inhibitors. We observed a significantly higher risk of bleeding with the combination of apixaban and felodipine (ROR 118.84, 95% CI 78.12-180.79, additive model 0.545, multiplicative model 1.253, PRR 22.896 (2450.141)). Moreover, consistent associations were found in the co-administration analyzes of rivaroxaban with dronedarone and diltiazem, and apixaban with losartan, telmisartan, and simvastatin. CONCLUSION: Our FAERS data analysis unveils varying degrees of bleeding risk associated with the co-administration of P-gp inhibitors and DOACs, underscoring the importance of vigilance about them in clinical practice.

Exploring keratin composition variability for sustainable thermal insulator design.

In pursuing sustainable thermal insulation solutions, this study explores the integration of human hair and feather keratin with alginate. The aim is to assess its potential in thermal insulation materials, focusing on the resultant composites' thermal and mechanical characteristics. The investigation uncovers that the type and proportion of keratin significantly influence the composites' porosity and thermal conductivity. Specifically, higher feather keratin content is associated with lesser sulfur and reduced crosslinking due to shorter amino acids, leading to increased porosity and pore sizes. This, in turn, results in a decrease in ß-structured hydrogen bond networks, raising non-ordered protein structures and diminishing thermal conductivity from 0.044 W/(m·K) for pure alginate matrices to between 0.033 and 0.038 W/(m·K) for keratin-alginate composites, contingent upon the specific ratio of feather to hair keratin used. Mechanical evaluations further indicate that composites with a higher ratio of hair keratin exhibit an enhanced compressive modulus, ranging from 60 to 77 kPa, demonstrating the potential for tailored mechanical properties to suit various applications. The research underscores the critical role of sulfur content and the crosslinking index within keratin's structures, significantly impacting the thermal and mechanical properties of the matrices. The findings position keratin-based composites as environmentally friendly alternatives to traditional insulation materials.

The Quantitative Evaluation of Automatic Segmentation in Lumbar Magnetic Resonance Images.

OBJECTIVE: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. METHODS: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net's segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. RESULTS: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. CONCLUSION: Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Diagnostic meta-analysis of the efficacy of ultrasonography for diagnosing carpal tunnel syndrome: A comparison between Asian and non-Asian populations.

BACKGROUND: Ultrasonography is used to diagnose carpal tunnel syndrome (CTS) according to various criteria. This diagnostic meta-analysis aimed to evaluate the efficacy of ultrasonography for diagnosing CTS, focusing on the cross-sectional area (CSA) of the median nerve (MN) at the inlet of the carpal tunnel and regional variations in diagnostic thresholds between Asian and non-Asian populations. METHODS: A comprehensive literature search was conducted using PubMed, Embase, and the Cochrane Library. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Patient demographic data, diagnostic "gold standards", CSA cutoff values, and diagnostic results were extracted. Meta-analysis was performed to determine the sensitivity, specificity, and optimal CSA cutoff values. RESULTS: For the 25 included studies, a combined sensitivity of 88% and specificity of 84% for CSA measurements at the carpal tunnel inlet were obtained. The Asian group had a sensitivity of 84% and specificity of 86%, while the non-Asian group had a sensitivity of 91% and specificity of 82%. The mean CSA in the Asian group was significantly lower than that in the non-Asian group (12.93 mm2 and 14.77 mm2, respectively; p = 0.042). For the Asian group, the summary receiver operating characteristic curve had an area under the curve (AUC) of 0.92 with an optimal cutoff of 10.5 mm2; for the non-Asian group, an AUC of 0.94 was obtained with a cutoff of 11.5 mm2. CONCLUSION: Ultrasonography is a reliable diagnostic method for CTS, with distinct optimal cutoff values observed between Asian and non-Asian populations. Therefore, population-specific diagnostic criteria for CTS are recommended.

Cultivation of novel Atribacterota from oil well provides new insight into their diversity, ecology, and evolution in anoxic, carbon-rich environments.

BACKGROUND: The Atribacterota are widely distributed in the subsurface biosphere. Recently, the first Atribacterota isolate was described and the number of Atribacterota genome sequences retrieved from environmental samples has increased significantly; however, their diversity, physiology, ecology, and evolution remain poorly understood. RESULTS: We report the isolation of the second member of Atribacterota, Thermatribacter velox gen. nov., sp. nov., within a new family Thermatribacteraceae fam. nov., and the short-term laboratory cultivation of a member of the JS1 lineage, Phoenicimicrobium oleiphilum HX-OS.bin.34TS, both from a terrestrial oil reservoir. Physiological and metatranscriptomics analyses showed that Thermatribacter velox B11T and Phoenicimicrobium oleiphilum HX-OS.bin.34TS ferment sugars and n-alkanes, respectively, producing H2, CO2, and acetate as common products. Comparative genomics showed that all members of the Atribacterota lack a complete Wood-Ljungdahl Pathway (WLP), but that the Reductive Glycine Pathway (RGP) is widespread, indicating that the RGP, rather than WLP, is a central hub in Atribacterota metabolism. Ancestral character state reconstructions and phylogenetic analyses showed that key genes encoding the RGP (fdhA, fhs, folD, glyA, gcvT, gcvPAB, pdhD) and other central functions were gained independently in the two classes, Atribacteria (OP9) and Phoenicimicrobiia (JS1), after which they were inherited vertically; these genes included fumarate-adding enzymes (faeA; Phoenicimicrobiia only), the CODH/ACS complex (acsABCDE), and diverse hydrogenases (NiFe group 3b, 4b and FeFe group A3, C). Finally, we present genome-resolved community metabolic models showing the central roles of Atribacteria (OP9) and Phoenicimicrobiia (JS1) in acetate- and hydrocarbon-rich environments. CONCLUSION: Our findings expand the knowledge of the diversity, physiology, ecology, and evolution of the phylum Atribacterota. This study is a starting point for promoting more incisive studies of their syntrophic biology and may guide the rational design of strategies to cultivate them in the laboratory. Video Abstract.

Investigation of Lithium-Ion Battery Performance Utilizing Magnetic Controllable Superionic Conductor Li3(V1-x Fe x )2(PO4)3/C (x = 0.05 and 0.10).

Lithium-ion batteries with Li3V2(PO4)3/C as the cathode have been a popular research topic in recent years; however, studies of the effects of external magnetic fields on them are less common. This study investigates the effects of an external magnetic field applied parallel to the direction of the anode and cathode on the ion transport through iron-doped Li3(V1-x Fe x )2(PO4)3, the outer carbon coating, the film/electrolyte/separator, and up to the lithium metal electrode on a microscopic level. The results reveal that for the x = 0.05 sample with lower doping, the magnetostriction expansion of Li3(V1-x Fe x )2(PO4)3 and the magnetostrictive contraction effect of the outer ordered carbon layer cancel each other out, resulting in no significant enhancement of the battery's energy and power density due to the external magnetic field. In contrast, the x = 0.1 sample, lacking magnetostrictive contraction in the outer ordered carbon layer, shows that its energy and power density can be influenced by the magnetic field. Under zero magnetic field, the cyclic performance exhibits superior average capacity performance in the x = 0.05 sample, while the x = 0.1 sample shows a lower decay rate. Both samples are affected by the magnetic field; however, the x = 0.1 sample performs better under magnetic conditions. In particular, in the C-rate tests under a magnetic field, the sample with x = 0.1 showed a significant relative reduction in capacity decay rate by 20.18% compared to the sample with x = 0.05.

Modelling individual differences in reading using an optimised MikeNet simulator: the impact of reading instruction.

Reading is vital for acquiring knowledge and studies have demonstrated that phonology-focused interventions generally yield greater improvements than meaning-focused interventions in English among children with reading disabilities. However, the effectiveness of reading instruction can vary among individuals. Among the various factors that impact reading skills like reading exposure and oral language skills, reading instruction is critical in facilitating children's development into skilled readers; it can significantly influence reading strategies, and contribute to individual differences in reading. To investigate this assumption, we developed a computational model of reading with an optimised MikeNet simulator. In keeping with educational practices, the model underwent training with three different instructional methods: phonology-focused training, meaning-focused training, and phonology-meaning balanced training. We used semantic reliance (SR), a measure of the relative reliance on print-to-sound and print-to-meaning mappings under the different training conditions in the model, as an indicator of individual differences in reading. The simulation results demonstrated a direct link between SR levels and the type of reading instruction. Additionally, the SR scores were able to predict model performance in reading-aloud tasks: higher SR scores were correlated with increased phonological errors and reduced phonological activation. These findings are consistent with data from both behavioral and neuroimaging studies and offer insights into the impact of instructional methods on reading behaviors, while revealing individual differences in reading and the importance of integrating OP and OS instruction approaches for beginning readers.

Double contrast-enhanced ultrasonography improves diagnostic accuracy of T staging compared with multi-detector computed tomography in gastric cancer patients.

BACKGROUND: Gastric cancer (GC) is the most common malignant tumor and ranks third for cancer-related deaths among the worldwide. The disease poses a serious public health problem in China, ranking fifth for incidence and third for mortality. Knowledge of the invasive depth of the tumor is vital to treatment decisions. AIM: To evaluate the diagnostic performance of double contrast-enhanced ultrasonography (DCEUS) for preoperative T staging in patients with GC by comparing with multi-detector computed tomography (MDCT). METHODS: This single prospective study enrolled patients with GC confirmed by preoperative gastroscopy from July 2021 to March 2023. Patients underwent DCEUS, including ultrasonography (US) and intravenous contrast-enhanced ultrasonography (CEUS), and MDCT examinations for the assessment of preoperative T staging. Features of GC were identified on DCEUS and criteria developed to evaluate T staging according to the 8th edition of AJCC cancer staging manual. The diagnostic performance of DCEUS was evaluated by comparing it with that of MDCT and surgical-pathological findings were considered as the gold standard. RESULTS: A total of 229 patients with GC (80 T1, 33 T2, 59 T3 and 57 T4) were included. Overall accuracies were 86.9% for DCEUS and 61.1% for MDCT (P < 0.001). DCEUS was superior to MDCT for T1 (92.5% vs 70.0%, P < 0.001), T2 (72.7% vs 51.5%, P = 0.041), T3 (86.4% vs 45.8%, P < 0.001) and T4 (87.7% vs 70.2%, P = 0.022) staging of GC. CONCLUSION: DCEUS improved the diagnostic accuracy of preoperative T staging in patients with GC compared with MDCT, and constitutes a promising imaging modality for preoperative evaluation of GC to aid individualized treatment decision-making.

Teleost-specific TLR23 in Takifugu rubripes recruits MyD88 to trigger ERK pathway and promotes antibacterial defense.

Takifugu rubripes is a highly valued cultured fish in Asia, while pathogen infections can result in severe diseases and lead to substantial economic losses. Toll-like receptors (TLRs), as pattern recognition receptors, play a crucial role on recognition pathogens and initiation innate immune response. However, the immunological properties of teleost-specific TLR23 remain largely unknown. In this study, we investigated the biological functions of TLR23 (TrTLR23) from T. rubripes, found that TrTLR23 existed in various organs. Following bacterial pathogen challenge, the expression levels of TrTLR23 were significantly increased in immune related organs. TrTLR23 located on the cellular membrane and specifically recognized pathogenic microorganism. Co-immunoprecipitation and antibody blocking analysis revealed that TrTLR23 recruited myeloid differentiation primary response protein (MyD88), thereby mediating the activation of the ERK signaling pathway. Furthermore, in vivo showed that, when TrTLR23 is overexpressed in T. rubripes, bacterial replication in fish tissues is significantly inhibited. Consistently, when TrTLR23 expression in T. rubripes is knocked down, bacterial replication is significantly enhanced. In conclusion, these findings suggested that TrTLR23 played a critical role on mediation TLR23-MyD88-ERK axis against bacterial infection. This study revealed that TLR23 involved in the innate immune mechanism, and provided the foundation for development disease control strategies in teleost.

Identification of chronic non-atrophic gastritis and intestinal metaplasia stages in the Correa's cascade through machine learning analyses of SERS spectral signature of non-invasively-collected human gastric fluid samples.

The progression of gastric cancer involves a complex multi-stage process, with gastroscopy and biopsy being the standard procedures for diagnosing gastric diseases. This study introduces an innovative non-invasive approach to differentiate gastric disease stage using gastric fluid samples through machine-learning-assisted surface-enhanced Raman spectroscopy (SERS). This method effectively identifies different stages of gastric lesions. The XGBoost algorithm demonstrates the highest accuracy of 96.88% and 91.67%, respectively, in distinguishing chronic non-atrophic gastritis from intestinal metaplasia and different subtypes of gastritis (mild, moderate, and severe). Through blinded testing validation, the model can achieve more than 80% accuracy. These findings offer new possibilities for rapid, cost-effective, and minimally invasive diagnosis of gastric diseases.

Synthesis of a collection of nootkatone analogues with diverse skeletons.

A collection of ring distorted analogue of Nootkatone including 6 CTD (Complex to Diversity) compounds and 9 SAR (Structure Activity Relationship) compounds were synthesized utilizing the carbonyl group as a starting reaction point.