Switch to fixed-dose doravirine (100 mg) with islatravir (0·75 mg) once daily in virologically suppressed adults with HIV-1 on bictegravir, emtricitabine, and tenofovir alafenamide: 48-week results of a phase 3, randomised, controlled, double-blind, non-inferiority trial.

BACKGROUND: Doravirine and islatravir is an investigational, once-daily regimen with high antiviral potency, favourable safety and tolerability, and a low propensity for resistance. We investigated a switch from bictegravir, emtricitabine, and tenofovir alafenamide to doravirine (100 mg) and islatravir (0·75 mg) in virologically suppressed adults with HIV-1. METHODS: We conducted a phase 3, multicentre, randomised, active-controlled, double-blind, double-dummy, non-inferiority trial at 89 research, community, and hospital-based clinics in 11 countries. Adults aged 18 years or older with fewer than 50 HIV-1 RNA copies per mL for at least 3 months on bictegravir (50 mg), emtricitabine (200 mg), and tenofovir alafenamide (25 mg) and no history of previous virological failure on any past or current regimen were randomly assigned (1:1) by a computer-generated randomisation allocation schedule, with block randomisation based on a block size of four, to switch to doravirine (100 mg) and islatravir (0·75 mg) or continue bictegravir, emtricitabine, and tenofovir alafenamide orally once daily, with matching placebos taken by all participants. Participants, investigators, study staff, and sponsor personnel involved in study drug administration or clinical evaluation of participants were masked to treatment assignment until week 48. Participants were instructed at each visit to take one tablet from each of the two bottles received, one of study drug and one of placebo, once daily, and participants were assessed at baseline and weeks 4, 12, 24, 36, and 48. The primary endpoint was the proportion of participants with greater than or equal to 50 HIV-1 RNA copies per mL at week 48 in the full analysis set (ie, all participants who received at least one dose of study drug; US Food and Drug Administration snapshot; prespecified non-inferiority margin 4%). The study is ongoing, with all remaining participants in post-treatment follow-up, and is registered with ClinicalTrials.gov, NCT04223791. FINDINGS: We screened 726 individuals for eligibility between Feb 18 and Sept 3, 2020, of whom 643 (88·6%) participants were randomly assigned to a treatment group (183 [28·5%] women and 460 [71·5%] men). 322 participants were switched to doravirine (100 mg) and islatravir (0·75 mg) and 321 continued bictegravir, emtricitabine, and tenofovir alafenamide (two participants [one with a protocol deviation and one who withdrew] assigned to bictegravir, emtricitabine, and tenofovir alafenamide did not receive treatment). The last follow-up visit for the week 48 analysis occurred on Aug 26, 2021. At week 48, two (0·6%) of 322 participants in the doravirine and islatravir group compared with one (0·3%) of 319 participants in the bictegravir, emtricitabine, and tenofovir alafenamide group had greater than or equal to 50 HIV-1 RNA copies per mL (difference 0·3%, 95% CI -1·2 to 2·0). The per-protocol analysis showed consistent results. 25 (7·8%) participants in the doravirine and islatravir group had headache compared with 23 [7·2%] participants in the bictegravir, emtricitabine, and tenofovir alafenamide group; 101 (31·4%) compared with 98 (30·7%) had infections; and eight (2·5%) participants in each group discontinued therapy due to adverse events. 32 (9·9%) participants had treatment-related adverse events in the islatravir and doravirine group comapred with 38 (11·9%) in the bictegravir, emtricitabine, and tenofovir alafenamide group. In the islatravir and doravirine group, CD4 cell counts (mean change -19·7 cells per µL) and total lymphocyte counts (mean change -0·20 × 109/L) were decreased at 48 weeks. INTERPRETATION: Switching to daily doravirine (100 mg) and islatravir (0·75 mg) was non-inferior to bictegravir, emtricitabine, and tenofovir alafenamide at week 48. However, decreases in CD4 cell and total lymphocyte counts do not support the further development of once-daily doravirine (100 mg) and islatravir (0·75 mg). FUNDING: Merck Sharp & Dohme, a subsidiary of Merck & Co.

Chitosan-based GOx@Co-MOF composite hydrogel: A promising strategy for enhanced antibacterial and wound healing effects.

A novel composite hydrogel was synthesized via Schiff base reaction between chitosan and di-functional poly(ethylene glycol) (DF-PEG), incorporating glucose oxidase (GOx) and cobalt metal-organic frameworks (Co-MOF). The resulting CS/PEG/GOx@Co-MOF composite hydrogel was characterized using Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA), and energy-dispersive X-ray spectroscopy (EDS). The results confirmed successful integration and uniform distribution of Co-MOF within the hydrogel matrix. Functionally, the hydrogel exploits the catalytic decomposition of glucose by GOx to generate gluconic acid and hydrogen peroxide (H2O2), while Co-MOF gradually releases metal ions and protects GOx. This synergy enhanced the antibacterial activity of the composite hydrogel against both Gram-positive (S. aureus) and Gram-negative bacteria (E. coli), outperforming conventional chitosan-based hydrogels. The potential of the composite hydrogel in treating wound infections was evaluated through antibacterial and wound healing experiments. Overall, CS/PEG/GOx@Co-MOF hydrogel holds great promise for the treatment of wound infections, paving the way for further research and potential clinical applications.

Association Between Preoperative Sleep Disturbance and Postoperative Delirium in Elderly: A Retrospective Cohort Study.

Purpose: Postoperative sleep disturbance, characterized by diminished postoperative sleep quality, is a risk factor for postoperative delirium (POD); however, the association between pre-existing sleep disturbance and POD remains unclear. This study aimed to evaluate the association between preoperative sleep disturbance and POD in elderly patients after non-cardiac surgery. Patients and methods: This retrospective cohort study was conducted at a single center and enrolled 489 elderly patients who underwent surgery between May 1, 2020, and March 31, 2021. Patients were divided into the sleep disorder (SD) and non-sleep disorder (NSD) groups according to the occurrence of one or more symptoms of insomnia within one month or sleep- Numerical Rating Scale (NRS)≥6 before surgery. The primary outcome was the incidence of POD. Propensity score matching analysis was performed between the two groups. Multiple logistic regression analysis was performed to identify the risk factors for POD. Results: In both the unmatched cohort (16.0% vs 6.7%, P=0.003) and the matched cohort (17.0% vs 6.2%, P=0.023), the incidence of POD was higher in the SD group than in the NSD group. In addition, the postoperative sleep quality and the VAS score at postoperative 24 h were significantly lower in the SD group than in the NSD group. Multivariate logistic regression analysis indicated that age (Odds Ratio, 1.13 [95% CI: 1.04-1.23], P=0.003) and preoperative sleep disturbance (Odds Ratio, 3.03 [95% CI: 1.09-9.52], P=0.034) were independent risk factors for the development of POD. Conclusion: The incidence of POD was higher in patients with pre-existing sleep disturbance than those without it. Whether improving sleep quality for preoperative sleep disturbance may help prevent POD remains to be determined.

Two-dimensional radial-π-stacks in solution.

Highly organized π-aggregate architectures can strongly affect electronic couplings, leading to important photophysical behaviors. With the escalating interest in two-dimensional (2D) materials attributed to their exceptional electronic and optical characteristics, there is growing anticipation that 2D radial-π-stacks built upon radial π-conjugation nanorings, incorporating intra- and inter-ring electronic couplings within the confines of a 2D plane, will exhibit superior topological attributes and distinct properties. Despite their immense potential, the design and synthesis of 2D π-stacks have proven to be a formidable challenge due to the insufficient π-π interactions necessary for stable stacking. In this study, we present the successful preparation of single-layer 2D radial-π-stacks in a solution. Pillar-shaped radially π-conjugated [4]cyclo-naphthodithiophene diimide ([4]C-NDTIs) molecules were tetragonally arranged via in-plane intermolecular π-π interactions. These 2D π-stacks have a unique topology that differs from that of conventional 1D π-stacks and exhibit notable properties, such as acting as a 2D template capable of absorbing C60 guest molecules and facilitating the formation of 2D radial-π-stacks comprising [4]C-NDTI-C60 complexes, rapid exciton delocalization across the 2D plane, and efficient excitation energy funneling towards a trap.

Dispositional mindfulness: Is it related to knee osteoarthritis population's common health problems?

BACKGROUND: A growing body of research supports dispositional mindfulness as important in influencing physical and mental health as well as physical activities in patients with chronic illnesses. Knee osteoarthritis (OA), which often causes health problems, is one of the most common chronic illnesses, but less is known about dispositional mindfulness in relation to this condition. OBJECTIVE: To explore possible associations between dispositional mindfulness and physical and mental health as well as physical activity in knee OA patients. METHODS: For this cross-sectional study, we recruited a purposive sample of orthopedic clinic patients in hospitals in Southern Taiwan. Instruments included the Mindful Attention Awareness Scale (MAAS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Health-related characteristics were also measured. Demographic statistics, bivariate correlations, and multiple linear regression informed our exploration of potentially related factors for dispositional mindfulness. RESULTS: Participants with knee OA (N = 250) were mostly elderly (88%), female (70.5%), and married (84%). Marital status, perceived health status, depression, and physical activity were associated with dispositional mindfulness. Better perceived health, lower depression, and greater physical activity were significantly associated with greater dispositional mindfulness. However, the severity of symptoms, fear of falling, and exercise self-efficacy did not reach statistical significance in relation to dispositional mindfulness. CONCLUSION: Greater emphasis should be placed on the cultivation of mindfulness to enhance individuals' perceived health, decrease their depressive mood, and promote their engagement in physical activity, which could indirectly alleviate their experience of pain and improve their physical function, yielding better quality of life and well-being. Future research should focus on interventions to apply dispositional mindfulness in order to determine whether dispositional mindfulness can effectively improve physical and mental health as well as physical activity in those with knee OA.

[Influencing mechanism of stand age to the accumulation of microbial residue carbon in the Pinus masso-niana plantations]. / 林龄对马尾松人工林微生物残体碳积累的影响机制.

Clarifying the accumulation pattern of soil microbial residue carbon and its contribution to soil organic carbon (SOC) across stand age is helpful to understand the mechanism underlying soil carbon cycling. In this study, we analyzed the differences of amino sugar content, physicochemical properties and microbial composition in surface soil (0-10 cm) in young (6 a), middle-aged (13 a), near-mature (29 a), mature (38 a) and over-mature (57 a) Pinus massoniana plantations of subtropical China, quantified the microbial residue carbon content and its contribution to SOC, and discussed the mechanism. The results showed that SOC, total nitrogen, amorphous iron oxide and leucine aminopeptidase contents in the middle-aged plantation were significantly lower than those in the mature plantation. Soil pH and fungal/bacteria in young plantation were significantly higher than those in other age groups. Across the stand age gradient, the ranges of microbial, fungal and bacterial residue carbon were 7.52-14.63, 4.03-8.00 and 3.48-6.63 g·kg-1, respectively. The contents of all the residue carbon were significantly higher in the mature plantation than that of the middle-aged plantation, which were positively affected by soil total nitrogen content. The contribution of microbial, fungal, and bacterial residue carbon to SOC was 59.7%-72.3%, 33.4%-45.6%, and 24.3%-30.8%, respectively. The contribution of fungal residue carbon to SOC in young plantation was significantly higher than that in other age groups, and the contribution of bacterial residue carbon to SOC in middle-aged plantation was significantly higher than that in young and near-mature plantations, both of which were affected by soil inorganic nitrogen. Fungal residue carbon content was 1.2-1.7 times as that of bacterial residue carbon content, and dominated for the accumulation of microbial residue carbon. Results of the partial least squares model showed that stand age, soil environmental factors (such as leucine aminopeptidase, amorphous iron oxide, pH, and total nitrogen), bacterial residue carbon, fungal residue carbon and the contribution of bacterial residue carbon to SOC had total effects on the contribution of fungal residue carbon to SOC (-0.37, -1.16, 0.90, 1.09, and 0.83, respectively). In conclusion, stand age promoted the accumulation of microbial residue carbon but did not increase its contribution to SOC.

Broadband light absorption by a hemispherical concentric nanoshell array.

Achieving highly efficient broadband absorption is an important research area in nanophotonics. In this paper, a novel method is proposed to design broadband near-perfect absorbers, consisting of a four-layer hemispherical concentric nanoshell array. The proposed nanostructure supports absorptivity exceeding 95% in the entire visible region, and the absorption bandwidth is determined by the interaction or 'hybridization' of the plasmons of the inner and outer metal-based nanoshells. Moreover, the designed absorber has wide-angle capability and is insensitive to polarization. The simple structure, as well as the stable absorption properties, suggests that such core-shell nanostructures can serve as a potential candidate for many applications such as solar energy harvesting, photo-detection, and emissivity control.

The gut microbiome modulates the susceptibility to traumatic stress in a sex-dependent manner.

Post-traumatic stress disorder (PTSD), a psychological condition triggered by exposure to extreme or chronic stressful events, exhibits a sex bias in incidence and clinical manifestations. Emerging research implicates the gut microbiome in the pathogenesis of PTSD and its roles in stress susceptibility. However, it is unclear whether differential gut microbiota contribute to PTSD susceptibility in male and female rats. Here, we utilized the single prolonged stress animal model and employed unsupervised machine learning to classify stressed animals into stress-susceptible subgroups and stress-resilient subgroups. Subsequently, using 16S V3-V4 rDNA sequencing, we investigated the differential gut microbiota alterations between susceptible and resilient individuals in male and female rats. Our findings revealed distinct changes in gut microbiota composition between the sexes at different taxonomic levels. Furthermore, the abundance of Parabacteroides was lower in rats that underwent SPS modeling compared to the control group. In addition, the abundance of Tenericutes in the stress-susceptible subgroup was higher than that in the control group and stress-resilient subgroup, suggesting that Tenericutes may be able to characterize stress susceptibility. What is particularly interesting here is that Cyanobacteria may be particularly associated with anti-anxiety effects in male rats. This study underscores sex-specific variations in gut microbiota composition in response to stress and sex differences should be taken into account when using macrobiotics for neuropsychiatric treatment, highlighting potential targets for PTSD therapeutic interventions.

Monitoring Hierarchical Assembly of Ring-in-Ring and Russian Doll Complexes Based on Carbon Nanoring by Förster Resonance Energy Transfer.

We presented the construction of the ring-in-ring and Russian doll complexes on the basis of triptycene-derived carbon nanoring (TP-[12]CPP), which not only acts as a host for pillar[5]arene (P5A) but also serves as an energy donor for building Förster resonance energy transfer (FRET) systems. We also demonstrated that their hierarchical assembly processes could be efficiently monitored in real time using FRET. NMR, UV-vis and fluorescence, and mass spectroscopy analyses confirmed the successful encapsulation of the guests P5A/P5A-An by TP-[12]CPP, facilitated by C-H···π and ···π interactions, resulting in the formation of a distinct ring-in-ring complex with a binding constant of Ka = 2.23 × 104 M-1. The encapsulated P5A/P5A-An can further reverse its role to be a host for binding energy acceptors to form Russian doll complexes, as evidenced by the occurrence of FRET and mass spectroscopy analyses. The apparent binding constant of the Russian doll complexes was up to 3.6 × 104 M-1, thereby suggesting an enhanced synergistic effect. Importantly, the Russian doll complexes exhibited both intriguing one-step and sequential FRET dependent on the subcomponent P5A/P5A-An during hierarchical assembly, reminiscent of the structure and energy transfer of the light-harvesting system presented in purple bacteria.

Minimally invasive puncture combined with a high frequency of urokinase therapy improves outcomes in patients with HICH.

Minimally invasive puncture combined with urokinase is widely used in the treatment of hypertensive intracerebral hemorrhage (HICH). However, the appropriate frequency of urokinase following minimally invasive puncture in patients is still unclear. In total, 55 patients were enrolled in this study. According to the frequency of urokinase (10.0 â€‹× â€‹104 units) administration, 30 patients received urokinase at Q4h, while the other 25 patients received urokinase at Q8h. In the univariate analysis, preoperative GCS (p â€‹= â€‹0.0002), postoperative GCS (p â€‹= â€‹0.0007), the volume of residual hematoma (p â€‹= â€‹0.0179), and the frequency of urokinase (p â€‹= â€‹0.0110) were associated with unfavorable outcomes in patients with HICH in the basal ganglia. The multivariate analysis revealed that the frequency of urokinase was independently associated with unfavorable outcomes in patients with HICH in the basal ganglia (p â€‹= â€‹0.038, 1.109-35.380). The drainage time was significantly shorter in the Q4h group (14.17 â€‹± â€‹0.86 â€‹h) than in the Q8h group (27.36 â€‹± â€‹1.39 â€‹h) (p â€‹< â€‹0.0001). The GOS (4.37 â€‹± â€‹0.18), BI (75.52 â€‹± â€‹2.39), and mRS (1.67 â€‹± â€‹0.24) in the Q4h group were significantly ameliorated compared to those in the Q8h group (GOS 3.56 â€‹± â€‹0.18, BI 64.13 â€‹± â€‹2.22, and mRS 2.64 â€‹± â€‹0.28, respectively) (p â€‹= â€‹0.0004, p â€‹= â€‹0.0002, and p â€‹= â€‹0.0018) at 3 months of follow-up. Thus, minimally invasive puncture combined with urokinase is safe and efficient. Increasing the frequency of urokinase administration can produce faster and better postoperative recovery for patients with HICH in the basal ganglia.

Deep learning-driven diagnosis of multi-type vertebra diseases based on computed tomography images.

Background: Osteoporotic vertebral compression fractures (OVCFs) are the most common type of fragility fracture. Distinguishing between OVCFs and other types of vertebra diseases, such as old fractures (OFs), Schmorl's node (SN), Kummell's disease (KD), and previous surgery (PS), is critical for subsequent surgery and treatment. Combining with advanced deep learning (DL) technologies, this study plans to develop a DL-driven diagnostic system for diagnosing multi-type vertebra diseases. Methods: We established a large-scale dataset based on the computed tomography (CT) images of 1,051 patients with OVCFs from Luhe Hospital and used data of 46 patients from Xuanwu Hospital as alternative hospital validation dataset. Each patient underwent one examination. The dataset contained 11,417 CT slices and 19,718 manually annotated vertebrae with diseases. A two-stage DL-based system was developed to diagnose five vertebra diseases. The proposed system consisted of a vertebra detection module (VDModule) and a vertebra classification module (VCModule). Results: The training and testing dataset for the VDModule consisted of 9,135 and 3,212 vertebrae, respectively. The VDModule using the ResNet18-based Faster region-based convolutional neural network (R-CNN) model achieved an area under the curve (AUC), false-positive (FP) rate, and false-negative (FN) rate of 0.982, 1.52%, and 1.33%, respectively, in the testing dataset. The training dataset for VCModule consisted of 14,584 and 47,604 diseased and normal vertebrae, respectively. The testing dataset consisted of 4,489 and 15,122 diseased and normal vertebrae, respectively. The ResNet50-based VCModule achieved an average sensitivity and specificity of 0.919 and 0.995, respectively, in diagnosing four kinds of vertebra diseases except for SN in the testing dataset. In the alternative hospital validation dataset, the ResNet50-based VCModule achieved an average sensitivity and specificity of 0.891 and 0.989, respectively, in diagnosing four kinds of vertebra diseases except for SN. Conclusions: Our proposed DL system can accurately diagnose four vertebra diseases and has strong potential to facilitate the accurate and rapid diagnosis of vertebral diseases.

Risk Factors Analysis of Severe Liver Injury Induced by Statins.

The aim of this study is to report the risk factors of severe statin induced liver injury (SILI). From the database of Shandong ADR Monitoring Center and Outpatients and inpatients in our hospital, SILI cases reported from 2013 to 2021 were extracted and screened. The diagnostic criteria of SILI, the inclusion and exclusion criteria of severe and general SILI were established separately. After the SILI cases were selected and confirmed, the socio-demographic and clinical characteristics were collected. Single factor chi-square test and multi-factor unconditional logistic regression analysis were used to analyze the influencing factors of severe SILI. From 1391 reported cases, 1211 met SILI diagnostic criteria, of which 157 were severe SILI and 964 were general SILI. Univariate analysis showed that age, drug combination, statin category were the influencing factors of severe SILI (p<0.1). Multivariate logistic analysis showed that drug combination and statin category were the influencing factors of severe SILI (p<0.05). Atorvastatin caused the most serious SILI, and its risk is 1.77 times higher than rosuvastatin. The serious SILI risk of drug combination was 2.08 times higher than statin alone. The patient with these factors should be monitored intensively during clinical treatment, to ensure their medication safety.

Gastric Ganglioneuroblastoma in an Elderly man: A Case Report and Literature Review.

Background. Ganglioneuroblastoma is a borderline tumor of sympathetic origin that is considered a childhood disease, with the majority of patients occurring in children less than five years old and few patients occurring in adults. There are no treatment guidelines for adult ganglioneuroblastoma. Here, we report a rare patient of adult gastric ganglioneuroblastoma that was completely resected by a laparoscopic approach. Case presentation. A 73-year-old man presented with dull pain in the upper abdomen along with abdominal distension for one month. Gastroscopy examination revealed chronic gastritis and submucosal tumors of the gastric antrum. Endoscopic ultrasonography showed a hypoechoic mass in the gastric antrum arising from the muscularis propria. An abdominal computed tomography scan revealed an irregular soft tissue mass in the gastric antrum with heterogeneous enhancement in the arterial phase. The mass was completely resected by laparoscopic surgery. Postoperative histopathology revealed that the mass contained differentiated neuroblasts, mature ganglion cells and ganglioneuroma components. The pathological diagnosis was ganglioneuroblastoma intermixed, and the patient was determined to be in stage I. The patient received no adjuvant chemotherapy or radiotherapy. At his two-year follow-up, the patient was doing well and showed no signs of recurrence. Conclusion. Despite the rarity of gastric ganglioneuroblastoma as a primary site of origin, it should be considered in the differential diagnosis of gastric masses in adults. Radical surgery is sufficient for the treatment of ganglioneuroblastoma intermixed, and long-term follow-up should be performed.

An automatic diagnostic system for the urodynamic study applying in lower urinary tract dysfunction.

OBJECTIVE: To establish an automatic diagnostic system based on machine learning for preliminarily analysis of urodynamic study applying in lower urinary tract dysfunction (LUTD). METHODS: The eight most common conditions of LUTDs were included in the present study. A total of 527 eligible patients with complete data, from the year of 2015 to 2020, were enrolled in this study. In total, two global parameters (patients' age and sex) and 13 urodynamic parameters were considered to be the input for machine learning algorithms. Three machine learning approaches were applied and evaluated in this study, including Decision Tree (DT), Logistic Regression (LR), and Support Vector Machine (SVM). RESULTS: By applying machine learning algorithms into the 8 common LUTDs, the DT models achieved the AUC of 0.63-0.98, the LR models achieved the AUC of 0.73-0.99, and the SVM models achieved the AUC of 0.64-1.00. For mutually exclusive diagnoses of underactive detrusor and acontractile detrusor, we developed a classification model that classifies the patients into either of these two diseases or double-negative class. For this classification method, the DT models achieved the AUC of 0.82-0.85 and the SVM models achieved the AUC of 0.86-0.90. Among all these models, the LR and the SVM models showed better performance. The best model of these diagnostic tasks achieved an average AUC of 0.90 (0.90 ± 0.08). CONCLUSIONS: An automatic diagnostic system was developed using three machine learning models in urodynamic studies. This automated machine learning process could lead to promising assistance and enhancements of diagnosis and provide more useful reference for LUTD treatment.

ABHD6 drives endocytosis of AMPA receptors to regulate synaptic plasticity and learning flexibility.

Trafficking of α-Amino-3-hydroxy-5-methylisoxazole-4-propionic acid (AMPA) receptors (AMPARs), mediated by AMPAR interacting proteins, enabled neurons to maintain tuning capabilities at rest or active state. α/ß-Hydrolase domain-containing 6 (ABHD6), an endocannabinoid hydrolase, was an AMPAR auxiliary subunit found to negatively regulate the surface delivery of AMPARs. While ABHD6 was found to prevent AMPAR tetramerization in endoplasmic reticulum, ABHD6 was also reported to localize at postsynaptic site. Yet, the role of ABHD6 interacting with AMPAR at postsynaptic site, and the physiological significance of ABHD6 regulating AMPAR trafficking remains elusive. Here, we generated the ABHD6 knockout (ABHD6KO) mice and found that deletion of ABHD6 selectively enhanced AMPAR-mediated basal synaptic responses and the surface expression of postsynaptic AMPARs. Furthermore, we found that loss of ABHD6 impaired hippocampal long-term depression (LTD) and synaptic downscaling in hippocampal synapses. AMPAR internalization assays revealed that ABHD6 was essential for neuronal activity-dependent endocytosis of surface AMPARs, which is independent of ABHD6's hydrolase activity. The defects of AMPAR endocytosis and LTD are expressed as deficits in learning flexibility in ABHD6KO mice. Collectively, we demonstrated that ABHD6 is an endocytic accessory protein promoting AMPAR endocytosis, thereby contributes to the formation of LTD, synaptic downscaling and reversal learning.

Flexible Switching Pressure Sensors with Fast Response and Less Bending-Sensitive Performance Applied to Pain-Perception-Mimetic Gloves.

A strategy is proposed herein for preparing a flexible switching piezoresistive pressure sensor, which has a bridge-like structure and inverted micropyramids (IMPs) on its lower conductive substrate. The sensor substrates were prepared by injection compression molding (an industrial manufacturing process) using thermoplastic polyurethane (TPU; an industrial grade polymer). The designed bridge-like structure enables the sensor to obtain a pressure threshold. The flexibility of upper and lower TPU substrates allows them to contact quickly when pressed, and so the sensor exhibits a fast response (as short as 2 ms) and can respond to both static force and dynamic force (up to 50 Hz frequency), which are prominent for the sensor made from TPU. The sensor exhibits less bending-sensitive performance, which is attributed to the conformality of the upper and lower substrates and lower strain on the lower substrate with the IMP under bending. The sensor can amplify signal response at the monitoring limit (the relative resistance change is up to 46%). It can achieve a higher sensitivity in different low-pressure ranges by changing the gap of the bridge-like structure. Moreover, the sensor can obviously and steadily respond to an additional very low pressure under preloading and exhibits good durability performance. As the sensor has a pressure threshold similar to the human pain perception process, a pain-perception-mimetic glove that can identify the external mechanical stimuli but reduces the interference of finger bending is prepared, displaying potential applications of the flexible switching sensor in intelligent wearable protectors.

A fusion of VGG-16 and ViT models for improving bone tumor classification in computed tomography.

Background and Objective: Bone tumors present significant challenges in orthopedic medicine due to variations in clinical treatment approaches for different tumor types, which includes benign, malignant, and intermediate cases. Convolutional Neural Networks (CNNs) have emerged as prominent models for tumor classification. However, their limited perception ability hinders the acquisition of global structural information, potentially affecting classification accuracy. To address this limitation, we propose an optimized deep learning algorithm for precise classification of diverse bone tumors. Materials and Methods: Our dataset comprises 786 computed tomography (CT) images of bone tumors, featuring sections from two distinct bone species, namely the tibia and femur. Sourced from The Second Affiliated Hospital of Fujian Medical University, the dataset was meticulously preprocessed with noise reduction techniques. We introduce a novel fusion model, VGG16-ViT, leveraging the advantages of the VGG-16 network and the Vision Transformer (ViT) model. Specifically, we select 27 features from the third layer of VGG-16 and input them into the Vision Transformer encoder for comprehensive training. Furthermore, we evaluate the impact of secondary migration using CT images from Xiangya Hospital for validation. Results: The proposed fusion model demonstrates notable improvements in classification performance. It effectively reduces the training time while achieving an impressive classification accuracy rate of 97.6%, marking a significant enhancement of 8% in sensitivity and specificity optimization. Furthermore, the investigation into secondary migration's effects on experimental outcomes across the three models reveals its potential to enhance system performance. Conclusion: Our novel VGG-16 and Vision Transformer joint network exhibits robust classification performance on bone tumor datasets. The integration of these models enables precise and efficient classification, accommodating the diverse characteristics of different bone tumor types. This advancement holds great significance for the early detection and prognosis of bone tumor patients in the future.

The development and validation of pathological sections based U-Net deep learning segmentation model for the detection of esophageal mucosa and squamous cell neoplasm.

Background: Deep learning methods have demonstrated great potential for processing high-resolution images. The U-Net model, in particular, has shown proficiency in the segmentation of biomedical images. However, limited research has examined the application of deep learning to esophageal squamous cell carcinoma (ESCC) segmentation. Therefore, this study aimed to develop deep learning segmentation systems specifically for ESCC. Methods: A Visual Geometry Group (VGG)-based U-Net neural network architecture was utilized to develop the segmentation models. A pathological image cohort of surgical specimens was used for model training and internal validation, with two additional endoscopic biopsy section cohort for external validation. Model efficacy was evaluated across several metrics including Intersection over Union (IOU), accuracy, positive predict value (PPV), true positive rate (TPR), specificity, dice similarity coefficient (DSC), area under the receiver operating characteristic curve (AUC), and F1-Score. Results: Surgical samples from ten patients were analyzed retrospectively, with each biopsy section cohort encompassing five patients. Transfer learning models based on U-Net weights yielded optimal results. For mucosa segmentation, the in internal validation achieved 93.81% IOU, with other parameters exceeding 96% (96.96% accuracy, 96.45% PPV, 96.65% TPR, 98.41% specificity, 96.81% DSC, 96.11% AUC, and 96.55% F1-Score). The tumor segmentation model attained an IOU of 91.95%, along with other parameters surpassing 95% (95.90% accuracy, 95.62% PPV, 95.71% TPR, 97.88% specificity, 95.81% DSC, 94.92% AUC, and 95.67% F1-Score). In the external validation for tumor segmentation model, IOU was 59.86% for validation database 1 (72.74% for accuracy, 76.03% for PPV, 77.17% for TPR, 83.80% for specificity, 74.89% for DSC, 71.83% for AUC, and 76.60% for F1-Score), and 50.88% for validation cohort 2 (68.03% for accuracy, 59.02% for PPV, 66.87% for TPR, 78.48% for specificity, 67.44% for DSC, 64.68% for AUC, and 62.70% for F1-Score). Conclusions: The models exhibited satisfactory results, paving the way for their potential deployment on standard computers and integration with other artificial intelligence models in clinical practice in the future. However, limited to the size of study, the generalizability of models is impaired in the external validation, larger pathological section cohort would be needed in future development to ensure robustness and generalization.

Chronic Hepatitis B: Treat all Who Are Viremic?

The main aim of antiviral therapy in patients with chronic hepatitis B (CHB) is to prevent disease progression and reduce the risk of hepatocellular carcinoma (HCC). In general, treatment is recommended for select patient groups viewed as being at higher risk of developing adverse outcomes from CHB. However, patients who do not meet treatment criteria under current international guidelines may still benefit from antiviral therapy to reduce CHB-related complications. Moreover, well-tolerated antiviral drugs that are highly effective at suppressing viral replication are now widely available, and withholding therapy from patients with viremia is increasingly controversial. In this article, we review traditional treatment paradigms and argue the merits of expanding treatment eligibility to patients with CHB who do not meet current treatment criteria.