Gestational valproic acid exposure enhances facial stimulation-evoked cerebellar mossy fiber-granule cell transmission via GluN2A subunit-containing NMDA receptor in offspring mice.

Valproic acid (VPA) is one of the most effective antiepileptic drugs, and exposing animals to VPA during gestation has been used as a model for autism spectrum disorder (ASD). Numerous studies have shown that impaired synaptic transmission in the cerebellar cortical circuits is one of the reasons for the social deficits and repetitive behavior seen in ASD. In this study, we investigated the effect of VPA exposure during pregnancy on tactile stimulation-evoked cerebellar mossy fiber-granule cell (MF-GC) synaptic transmission in mice anesthetized with urethane. Three-chamber testing showed that mice exposed to VPA mice exhibited a significant reduction in social interaction compared with the control group. In vivo electrophysiological recordings revealed that a pair of air-puff stimulation on ipsilateral whisker pad evoked MF-GC synaptic transmission, N1, and N2. The evoked MF-GC synaptic responses in VPA-exposed mice exhibited a significant increase in the area under the curve (AUC) of N1 and the amplitude and AUC of N2 compared with untreated mice. Cerebellar surface application of the selective N-methyl-D-aspartate (NMDA) receptor blocker D-APV significantly inhibited facial stimulation-evoked MF-GC synaptic transmission. In the presence of D-APV, there were no significant differences between the AUC of N1 and the amplitude and AUC of N2 in the VPA-exposed mice and those of the untreated mice. Notably, blockade of the GluN2A subunit-containing, but not the GluN2B subunit-containing, NMDA receptor, significantly inhibited MF-GC synaptic transmission and decreased the AUC of N1 and the amplitude and AUC of N2 in VPA-exposed mice to levels similar to those seen in untreated mice. In addition, the GluN2A subunit-containing NMDA receptor was expressed at higher levels in the GC layer of VPA-treated mice than in control mice. These results indicate that gestational VPA exposure in mice produces ASD-like behaviors, accompanied by increased cerebellar MF-GC synaptic transmission and an increase in GluN2A subunit-containing NMDA receptor expression in the offspring.

Smad4 regulates TGF-ß1-mediated hedgehog activation to promote epithelial-to-mesenchymal transition in pancreatic cancer cells by suppressing Gli1 activity.

Pancreatic cancer (PC) is an aggressive and metastatic gastrointestinal tumor with a poor prognosis. Persistent activation of the TGF-ß/Smad signaling induces PC cell (PCC) invasion and infiltration via epithelial-to-mesenchymal transition (EMT). Hedgehog signaling is a crucial pathway for the development of PC via the transcription factors Gli1/2/3. This study aimed to investigate the underlying molecular mechanisms of action of hedgehog activation in TGF-ß1-triggered EMT in PCCs (PANC-1 and BxPc-3). In addition, overexpression and shRNA techniques were used to evaluate the role of Smad4 in TGF-ß1-treated PCCs. Our data showed that TGF-ß1 promoted PCC invasion and infiltration via Smad2/3-dependent EMT. Hedgehog-Gli signaling axis in PCCs was activated upon TGF-ß1 stimulation. Inhibition of hedgehog with cyclopamine effectively antagonized TGF-ß1-induced EMT, thereby suggesting that the hedgehog signaling may act as a downstream cascade signaling of TGF-ß1. As a key protein that assists the nuclear translocation of Smad2/3, Smad4 was highly expressed in PANC-1 cells, but not in BxPc-3 cells. Conversely, Gli1 expression was low in PANC-1 cells, but high in BxPc-3 cells. Furthermore, knockdown of Smad4 in PANC-1 cells by shRNA inhibited TGF-ß1-mediated EMT and collagen deposition. Overexpression of Smad4 did not affect TGF-ß1-mediated EMT due to the lack of significant increase in nuclear expression of Smad4. Importantly, Gli1 activity was upregulated by Smad4 knockdown in PANC-1 cells and downregulated by Smad4 overexpression in BxPc-3 cells, indicating that Gli1 may be a negative target protein downstream of Smad4. Thus, Smad4 regulates TGF-ß1-mediated hedgehog activation to promote EMT in PCCs by suppressing Gli1 activity.

Has the agricultural cooperatives served each member fairly? A new perspective based on utilization level of member services.

With the rapid increase of the number of agricultural cooperatives in China, the problem of fake cooperatives has become more and more serious. The core problem is that some members do not use cooperative services, and elite capture phenomenon appears in the organization. Since services are one of the most important public goods attributes of cooperatives, it is important to ensure that more members use them. What are the factors that affect members' utilization level of cooperative services? Existing research does not provide a comprehensive answer. Based on the micro-survey data of 74 citrus cooperatives and 524 citrus members in China, the article found out that 50.9% of the members did not use any services provided by cooperatives, and only 20.04% of the members used cooperatives' sales services. So, this study empirically analyzes the factors that influence the use of cooperatives' services by puns model. The results show that quality of service, service convenience and mountain terrain promote the use of cooperative sales services for members. In addition, cooperative knowledge, planting area, surplus distribution, quality of service, and service convenience significantly increased the utilization leve of cooperative sales services by members. Finally, the study puts forward some suggestions, such as propagating cooperative sales service, improving the quality of cooperative sales service, perfecting cooperative distribution system.

Automated, fast, robust brain extraction on contrast-enhanced T1-weighted MRI in presence of brain tumors: an optimized model based on multi-center datasets.

OBJECTIVES: Existing brain extraction models should be further optimized to provide more information for oncological analysis. We aimed to develop an nnU-Net-based deep learning model for automated brain extraction on contrast-enhanced T1-weighted (T1CE) images in presence of brain tumors. METHODS: This is a multi-center, retrospective study involving 920 patients. A total of 720 cases with four types of intracranial tumors from private institutions were collected and set as the training group and the internal test group. Mann-Whitney U test (U test) was used to investigate if the model performance was associated with pathological types and tumor characteristics. Then, the generalization of model was independently tested on public datasets consisting of 100 glioma and 100 vestibular schwannoma cases. RESULTS: In the internal test, the model achieved promising performance with median Dice similarity coefficient (DSC) of 0.989 (interquartile range (IQR), 0.988-0.991), and Hausdorff distance (HD) of 6.403 mm (IQR, 5.099-8.426 mm). U test suggested a slightly descending performance in meningioma and vestibular schwannoma group. The results of U test also suggested that there was a significant difference in peritumoral edema group, with median DSC of 0.990 (IQR, 0.989-0.991, p = 0.002), and median HD of 5.916 mm (IQR, 5.000-8.000 mm, p = 0.049). In the external test, our model also showed to be robust performance, with median DSC of 0.991 (IQR, 0.983-0.998) and HD of 8.972 mm (IQR, 6.164-13.710 mm). CONCLUSIONS: For automated processing of MRI neuroimaging data presence of brain tumors, the proposed model can perform brain extraction including important superficial structures for oncological analysis. CLINICAL RELEVANCE STATEMENT: The proposed model serves as a radiological tool for image preprocessing in tumor cases, focusing on superficial brain structures, which could streamline the workflow and enhance the efficiency of subsequent radiological assessments. KEY POINTS: • The nnU-Net-based model is capable of segmenting significant superficial structures in brain extraction. • The proposed model showed feasible performance, regardless of pathological types or tumor characteristics. • The model showed generalization in the public datasets.

Versatile Neuromorphic Modulation and Biosensing based on N-type Small-molecule Organic Mixed Ionic-Electronic Conductors.

The ion/chemical-based modulation feature of organic mixed ionic-electronic conductors (OMIECs) are critical to advancing next generation bio-integrated neuromorphic hardware. Despite achievements with polymeric OMIECs in organic electrochemical neuronal synapse (OENS). However, small molecule OMIECs based OENS has not yet been realized. Here, for the first time, we demonstrate an effective materials design concept of combining n-type fused all-acceptor small molecule OMIECs with subtle side chain optimization that enables robustly and flexibly modulating versatile synaptic behavior and sensing neurotransmitter in solid or aqueous electrolyte, operating in accumulation modes. By judicious tuning the ending side chains, the linear oligoether and butyl chain derivative gNR-Bu exhibits higher recognition accuracy for a model artificial neural network (ANN) simulation, higher steady conductance states and more outstanding ambient stability, which is superior to the state-of-art n-type OMIECs based OENS. These superior artificial synapse characteristics of gNR-Bu can be attributed to its higher crystallinity with stronger ion bonding capacities. More impressively, we unprecedentedly realized n-type small-molecule OMIECs based OENS as a neuromorphic biosensor enabling to respond synaptic communication signals of dopamine even at sub-µM level in aqueous electrolyte. This work may open a new path of small-molecule ion-electron conductors for next-generation ANN and bioelectronics.

Epigenetic modification of m6A methylation: Regulatory factors, functions and mechanism in inflammatory bowel disease.

Although the exact cause of inflammatory bowel disease (IBD) is still unknown, there is a lot of evidence to support the notion that it results from a combination of environmental factors, immune system issues, gut microbial changes, and genetic susceptibility. In recent years, the role of epigenetics in the pathogenesis of IBD has drawn increasing attention. The regulation of IBD-related immunity, the preservation of the intestinal epithelial barrier, and autophagy are all significantly influenced by epigenetic factors. The most extensive epigenetic methylation modification of mammalian mRNA among them is N6-methyladenosine (m6A). It summarizes the general structure and function of the m6A regulating factors, as well as their complex effects on IBD by regulating the intestinal mucous barrier, intestine mucosal immunity, epidermal cell death, and intestinal microorganisms.This paper provides key insights for the future identification of potential new targets for the diagnosis and treatment of IBD.

A highly efficient lanthanide coordination polymer luminescent material for the multi-task detection of environmental pollutants.

It is challenging to explore novel-structure lanthanide coordination polymers (Ln-CPs) for sensing environmental pollutants. Herein, we designed and synthesized an organic bridging linker 3-(carboxymethoxy)-1-(carboxymethyl) pyrazole-4-carboxylic acid (H3ccpc), and then successfully prepared and characterized a novel Ln-CP, namely [Tb2(ccpc)2(H2O)6]·1.5H2O (ccpcTb). Structural analysis indicates that ccpcTb exhibits a two-dimensional structure, in which Tb ions are in an eight-coordinated environment. The photoluminescence performance of ccpcTb was discussed in detail. The ccpcTb displays bright green luminescence and behaves as a multi-responsive luminescent sensor toward Fe3+ ions, Cr2O72- ions and 2,4,6-trinitrophenol with high selectivity and low detection limits. Furthermore, the possible luminescence sensing mechanisms have been addressed in detail. The luminescence quenching mechanism of sensing Fe3+ and Cr2O72- is attributed to the energy competitive absorption, while that of sensing TNP is due to the synergistic effect of energy competitive absorption and photo-induced electron transfer.

Performance Test of a Well-Trained Model for Meningioma Segmentation in Health Care Centers: Secondary Analysis Based on Four Retrospective Multicenter Data Sets.

BACKGROUND: Convolutional neural networks (CNNs) have produced state-of-the-art results in meningioma segmentation on magnetic resonance imaging (MRI). However, images obtained from different institutions, protocols, or scanners may show significant domain shift, leading to performance degradation and challenging model deployment in real clinical scenarios. OBJECTIVE: This research aims to investigate the realistic performance of a well-trained meningioma segmentation model when deployed across different health care centers and verify the methods to enhance its generalization. METHODS: This study was performed in four centers. A total of 606 patients with 606 MRIs were enrolled between January 2015 and December 2021. Manual segmentations, determined through consensus readings by neuroradiologists, were used as the ground truth mask. The model was previously trained using a standard supervised CNN called Deeplab V3+ and was deployed and tested separately in four health care centers. To determine the appropriate approach to mitigating the observed performance degradation, two methods were used: unsupervised domain adaptation and supervised retraining. RESULTS: The trained model showed a state-of-the-art performance in tumor segmentation in two health care institutions, with a Dice ratio of 0.887 (SD 0.108, 95% CI 0.903-0.925) in center A and a Dice ratio of 0.874 (SD 0.800, 95% CI 0.854-0.894) in center B. Whereas in the other health care institutions, the performance declined, with Dice ratios of 0.631 (SD 0.157, 95% CI 0.556-0.707) in center C and 0.649 (SD 0.187, 95% CI 0.566-0.732) in center D, as they obtained the MRI using different scanning protocols. The unsupervised domain adaptation showed a significant improvement in performance scores, with Dice ratios of 0.842 (SD 0.073, 95% CI 0.820-0.864) in center C and 0.855 (SD 0.097, 95% CI 0.826-0.886) in center D. Nonetheless, it did not overperform the supervised retraining, which achieved Dice ratios of 0.899 (SD 0.026, 95% CI 0.889-0.906) in center C and 0.886 (SD 0.046, 95% CI 0.870-0.903) in center D. CONCLUSIONS: Deploying the trained CNN model in different health care institutions may show significant performance degradation due to the domain shift of MRIs. Under this circumstance, the use of unsupervised domain adaptation or supervised retraining should be considered, taking into account the balance between clinical requirements, model performance, and the size of the available data.

Prediction of upcoming urinary tract infection after intracerebral hemorrhage: a machine learning approach based on statistics collected at multiple time points.

Purpose: Accurate prediction of urinary tract infection (UTI) following intracerebral hemorrhage (ICH) can significantly facilitate both timely medical interventions and therapeutic decisions in neurocritical care. Our study aimed to propose a machine learning method to predict an upcoming UTI by using multi-time-point statistics. Methods: A total of 110 patients were identified from a neuro-intensive care unit in this research. Laboratory test results at two time points were chosen: Lab 1 collected at the time of admission and Lab 2 collected at the time of 48 h after admission. Univariate analysis was performed to investigate if there were statistical differences between the UTI group and the non-UTI group. Machine learning models were built with various combinations of selected features and evaluated with accuracy (ACC), sensitivity, specificity, and area under the curve (AUC) values. Results: Corticosteroid usage (p < 0.001) and daily urinary volume (p < 0.001) were statistically significant risk factors for UTI. Moreover, there were statistical differences in laboratory test results between the UTI group and the non-UTI group at the two time points, as suggested by the univariate analysis. Among the machine learning models, the one incorporating clinical information and the rate of change in laboratory parameters outperformed the others. This model achieved ACC = 0.773, sensitivity = 0.785, specificity = 0.762, and AUC = 0.868 during training and 0.682, 0.685, 0.673, and 0.751 in the model test, respectively. Conclusion: The combination of clinical information and multi-time-point laboratory data can effectively predict upcoming UTIs after ICH in neurocritical care.

Isotopic Characterization of Mercury Atmosphere-Foliage and Atmosphere-Soil Exchange in a Swiss Subalpine Coniferous Forest.

To understand the role of vegetation and soil in regulating atmospheric Hg0, exchange fluxes and isotope signatures of Hg were characterized using a dynamic flux bag/chamber at the atmosphere-foliage/soil interfaces at the Davos-Seehornwald forest, Switzerland. The foliage was a net Hg0 sink and took up preferentially the light Hg isotopes, consequently resulting in large shifts (-3.27‰) in δ202Hg values. The soil served mostly as net sources of atmospheric Hg0 with higher Hg0 emission from the moss-covered soils than from bare soils. The negative shift of δ202Hg and Δ199Hg values of the efflux air relative to ambient air and the Δ199Hg/Δ201Hg ratio among ambient air, efflux air, and soil pore gas highlight that Hg0 re-emission was strongly constrained by soil pore gas evasion together with microbial reduction. The isotopic mass balance model indicates 8.4 times higher Hg0 emission caused by pore gas evasion than surface soil photoreduction. Deposition of atmospheric Hg0 to soil was noticeably 3.2 times higher than that to foliage, reflecting the high significance of the soil to influence atmospheric Hg0 isotope signatures. This study improves our understanding of Hg atmosphere-foliage/soil exchange in subalpine coniferous forests, which is indispensable in the model assessment of forest Hg biogeochemical cycling.

Influence of Aging Treatment Regimes on Microstructure and Mechanical Properties of Selective Laser Melted 17-4 PH Steel.

Aging is indispensable for balancing the strength and ductility of selective laser melted (SLM) precipitation hardening steels. This work investigated the influence of aging temperature and time on the microstructure and mechanical properties of SLM 17-4 PH steel. The 17-4 PH steel was fabricated by SLM under a protective argon atmosphere (99.99 vol.%), then the microstructure and phase composition after different aging treatments were characterized via different advanced material characterization techniques, and the mechanical properties were systematically compared. Coarse martensite laths were observed in the aged samples compared with the as-built ones, regardless of the aging time and temperature. Increasing the aging temperature resulted in a larger grain size of the martensite lath and precipitation. The aging treatment induced the formation of the austenite phase with a face-centered cubic (FCC) structure. With prolonged aging treatment, the volume fraction of the austenite phase increased, which agreed with the EBSD phase mappings. The ultimate tensile strength (UTS) and yield strength gradually increased with increasing aging times at 482 °C. The UTS reached its peak value after aging for 3 h at 482 °C, which was similar to the trend of microhardness (i.e., UTS = 1353.4 MPa). However, the ductility of the SLM 17-4 PH steel decreased rapidly after aging treatment. This work reveals the influence of heat treatment on SLM 17-4 steel and proposes an optimal heat-treatment regime for the SLM high-performance steels.

Predicting visual recovery in pituitary adenoma patients post-endoscopic endonasal transsphenoidal surgery: Harnessing delta-radiomics of the optic chiasm from MRI.

OBJECTIVES: To investigate whether morphological changes after surgery and delta-radiomics of the optic chiasm obtained from routine MRI could help predict postoperative visual recovery of pituitary adenoma patients. METHODS: A total of 130 pituitary adenoma patients were retrospectively enrolled and divided into the recovery group (n = 87) and non-recovery group (n = 43) according to visual outcome 1 year after endoscopic endonasal transsphenoidal surgery. Morphological parameters of the optic chiasm were measured preoperatively and postoperatively, including chiasmal thickness, deformed angle, and suprasellar extension. Delta-radiomics of the optic chiasm were calculated based on features extracted from preoperative and postoperative coronal T2-weighted images, followed by machine learning modeling using least absolute shrinkage and selection operator wrapped with support vector machine through fivefold cross-validation in the development set. The delta-radiomic model was independently evaluated in the test set, and compared with the combined model that incorporated delta-radiomics, significant clinical and morphological parameters. RESULTS: Postoperative morphological changes of the optic chiasm could not significantly be used as predictors for the visual outcome. In contrast, the delta-radiomics model represented good performances in predicting visual recovery, with an AUC of 0.821 in the development set and 0.811 in the independent test set. Moreover, the combined model that incorporated age and delta-radiomics features of the optic chiasm achieved the highest AUC of 0.841 and 0.840 in the development set and independent test set, respectively. CONCLUSIONS: Our proposed machine learning models based on delta-radiomics of the optic chiasm can be used to predict postoperative visual recovery of pituitary adenoma patients. CLINICAL RELEVANCE STATEMENT: Our delta-radiomics-based models from MRI enable accurate visual recovery predictions in pituitary adenoma patients who underwent endoscopic endonasal transsphenoidal surgery, facilitating better clinical decision-making and ultimately improving patient outcomes. KEY POINTS: • Prediction of the postoperative visual outcome for pituitary adenoma patients is important but challenging. • Delta-radiomics of the optic chiasm after surgical decompression represented better prognostic performances compared with its morphological changes. • The proposed machine learning models can serve as novel approaches to predict visual recovery for pituitary adenoma patients in clinical practice.

STAT3 inhibition enhances gemcitabine sensitivity in pancreatic cancer by suppressing EMT, immune escape and inducing oxidative stress damage.

Pancreatic cancer (PC) is a highly-malignant tumor of the digestive system with a very poor prognosis and high mortality. Chemotherapy and PD-1/PD-L1 immune checkpoint blockade are important treatment strategies for advanced PC. However, chemotherapy resistance and poor therapeutic effect of immune checkpoint inhibitors is are the main clinical problems to be solved urgently at present. The effects of combined application of gemcitabine and STAT3 inhibition on the proliferation, apoptosis, migration, and invasion of PC cells (PCCs) were investigated. In addition, oxidative stress (OS), ferroptosis, immune escape, and the epithelial-mesenchymal transition (EMT) were evaluated. STAT3 inhibition with Stattic enhanced the inhibitory activity of gemcitabine on PCC proliferation by regulating the cell cycle. STAT3 inhibition enhanced mitochondrial-dependent apoptosis in gemcitabine-treated PCCs, but did not induce autophagy and ferroptosis. Further study showed that the anti-proliferative and pro-apoptotic effects may be associated with increased OS damage by inactivating Nrf2-HO-1 signaling, as well as DNA damage by inducing the imbalance between ATM andATR-Chk1 pathway. In addition, STAT3 inhibition strengthened gemcitabine-mediated suppression in PCC invasion and migration by antagonizing Smad2/3-dependent EMT. Moreover, the anti-tumorimmuneresponse of gemcitabine was upregulated by Stattic through reducing the expression of PD-L1 and CD47. Mechanistically, combined application of gemcitabine and Stattic suppressed the phosphorylation and nuclear expression of STAT3. Interestingly, the activities of AKT and ß-catenin signaling were also regulated, suggesting that drug combination has a broad-spectrum signal regulation effect. STAT3 inhibition enhanced the sensitivity of PCCs to the chemotherapy drug gemcitabine by suppressing EMT and immune escape and inducing OS damage.

Omentin-1 ameliorates experimental inflammatory bowel disease via Nrf2 activation and redox regulation.

AIMS: Omentin-1 production is decreased in patients with IBD. However, the specific role of Omentin-1 in IBD has not been fully elucidated. This study aimed to investigate the expression and role of Omentin-1 in IBD and the potential mechanisms. MAIN METHODS: We collected human serum and colon biopsy samples at the Wuhan Union Hospital. Omentin-1 recombinant protein was injected intraperitoneally in a DSS-induced experimental IBD mouse model. Omentin-1 levels were measured in IBD patients, colitis mice, and LPS-induced HT-29 cells. Omentin-1 and/or a Nrf2 specific inhibitor (ML385) were administered to DSS mice and LPS-induced HT-29 cells. The effects of Omentin-1 on inflammation, intestinal barrier function, Nrf2 pathway, oxidative stress, and NF-κB signaling were detected in vivo and in vitro. KEY FINDINGS: Serum Omentin-1 levels were significantly reduced in UC and CD patients compared with controls (173.7 (IQR, 120.1-221.2) ng/ml, 80.8 (43.8-151.8) ng/ml, and 270.7 (220.7-306.5) ng/ml, respectively). The levels of Omentin-1 were also significantly lower in colitis mice and LPS-induced HT-29 cells. Omentin-1 treatment effectively ameliorated inflammation and impaired intestinal barrier, decreased ROS and MDA levels, and increased GSH and SOD production in the DSS-induced colitis mice and LPS-induced HT-29 cells. Mechanically, Omentin-1 repaired the intestinal barrier by activating Nrf2, then improving oxidative stress and inhibiting NF-κB signaling. Furthermore, the interaction between Omentin-1 and Nrf2 was identified. SIGNIFICANCE: Omentin-1 activates the Nrf2 pathway to regulate redox balance, ultimately protecting intestinal barrier function and reducing intestinal inflammation. In general, Omentin-1 can be used as a promising therapeutic target for IBD.

NAD+ Metabolism and Immune Regulation: New Approaches to Inflammatory Bowel Disease Therapies.

Inflammatory bowel disease (IBD), which includes Crohn's disease (CD) and ulcerative colitis (UC), is a multifactorial systemic inflammatory immune response. Nicotinamide adenine dinucleotide (NAD+) is a co-enzyme involved in cell signaling and energy metabolism. Calcium homeostasis, gene transcription, DNA repair, and cell communication involve NAD+ and its degradation products. There is a growing recognition of the intricate relationship between inflammatory diseases and NAD+ metabolism. In the case of IBD, the maintenance of intestinal homeostasis relies on a delicate balance between NAD+ biosynthesis and consumption. Consequently, therapeutics designed to target the NAD+ pathway are promising for the management of IBD. This review discusses the metabolic and immunoregulatory processes of NAD+ in IBD to examine the molecular biology and pathophysiology of the immune regulation of IBD and to provide evidence and theoretical support for the clinical use of NAD+ in IBD.

Tunable control of the performance of aqueous-based electrochemical devices by post-polymerization functionalization.

Functionalized polymeric mixed ionic-electronic conductors (PMIECs) are highly desired for the development of electrochemical applications, yet are hindered by the limited conventional synthesis techniques. Here, we propose a "graft-onto-polymer" synthesis strategy by post-polymerization functionalization (GOP-PPF) to prepare a family of PMIECs sharing the same backbone while functionalized with varying ethylene glycol (EG) compositions (two, four, and six EG repeating units). Unlike the typical procedure, GOP-PPF uses a nucleophilic aromatic substitution reaction for the facile and versatile attachment of functional units to a pre-synthesized conjugated-polymer precursor. Importantly, these redox-active PMIECs are investigated as a platform for energy storage devices and organic electrochemical transistors (OECTs) in aqueous media. The ion diffusivity, charge mobility and charge-storage capacity can be significantly improved by optimizing the EG composition. Specifically, g2T2-gBT6 containing the highest EG density gives the highest charge-storage capacity exceeding 180 F g-1 among the polymer series, resulting from the improved ion diffusivity. Moreover, g2T2-gBT4 with four EG repeating units exhibits a superior performance compared to its two analogues in OECTs, associated with a high µC* up to 359 F V-1 cm-1 s-1, owing to the optimal balance between ionic-electronic coupling and charge mobility. Through the GOP-PPF, PMIECs can be tailored to access desirable performance metrics at the molecular level.

Predominant contributions through lichen and fine litter to litterfall mercury deposition in a subalpine forest.

Litterfall, typically referring to needles/leaves, may stand for >50% of the total mercury (Hg) deposition in forest ecosystems. By detailed categorisation, we reveal for the first time that the contributions through lichens and fine litter, together 9.98 µg Hg m-2 yr-1, could be as high as that in needle litter (9.96 µg m-2 yr-1) to the annual total Hg deposition (44.6 µg m-2 yr-1) in a subalpine forest in Switzerland. Noticeably, needle litter had the highest contribution (53%) to total Hg in the autumn litterfall but lichens and fine litter together predominated in other seasons (47-59%). Such a seasonal pattern is caused by the high ability of lichens and fine litter to accumulate Hg and the high needle litterfall in autumn, which is related to a good rainfall in summer followed by a dry period in autumn. The constantly higher Hg levels in lichens and fine litter than in needle litter together with similar seasonal patterns of litterfall during 2009-2019 and rainfall during 1980-2019 suggest that our finding can be generally valid. Here, we highlight not only the considerable role of non-needle litterfall in Hg deposition but also the association with weather for seasonal Hg dynamics in different litterfall components.

Preoperative volume of the optic chiasm is an easily obtained predictor for visual recovery of pituitary adenoma patients following endoscopic endonasal transsphenoidal surgery: a cohort study.

BACKGROUND: Predicting the postoperative visual outcome of pituitary adenoma patients is important but remains challenging. This study aimed to identify a novel prognostic predictor which can be automatically obtained from routine MRI using a deep learning approach. MATERIALS AND METHODS: A total of 220 pituitary adenoma patients were prospectively enrolled and stratified into the recovery and nonrecovery groups according to the visual outcome at 6 months after endoscopic endonasal transsphenoidal surgery. The optic chiasm was manually segmented on preoperative coronal T2WI, and its morphometric parameters were measured, including suprasellar extension distance, chiasmal thickness, and chiasmal volume. Univariate and multivariate analyses were conducted on clinical and morphometric parameters to identify predictors for visual recovery. Additionally, a deep learning model for automated segmentation and volumetric measurement of optic chiasm was developed with nnU-Net architecture and evaluated in a multicenter data set covering 1026 pituitary adenoma patients from four institutions. RESULTS: Larger preoperative chiasmal volume was significantly associated with better visual outcomes ( P =0.001). Multivariate logistic regression suggested it could be taken as the independent predictor for visual recovery (odds ratio=2.838, P <0.001). The auto-segmentation model represented good performances and generalizability in internal (Dice=0.813) and three independent external test sets (Dice=0.786, 0.818, and 0.808, respectively). Moreover, the model achieved accurate volumetric evaluation of the optic chiasm with an intraclass correlation coefficient of more than 0.83 in both internal and external test sets. CONCLUSION: The preoperative volume of the optic chiasm could be utilized as the prognostic predictor for visual recovery of pituitary adenoma patients after surgery. Moreover, the proposed deep learning-based model allowed for automated segmentation and volumetric measurement of the optic chiasm on routine MRI.

The Impact of Corneal Epithelial Thickening and Inhomogeneity on Corneal Aberrations After Small Incision Lenticule Extraction.

PURPOSE: To investigate the corneal epithelial remodeling profile after small incision lenticule extraction (SMILE), the correlated explanatory variables, and its potential impact on corneal higher order aberrations (HOAs). METHODS: This single-center study prospectively evaluated 75 right eyes of 75 patients scheduled for SMILE. An anterior segment optical coherence tomography device was used to automatically obtain central 6-mm corneal epithelial thickness (ET), total corneal HOAs, and individual Zernike components before and after surgery. The ET inhomogeneity over the central 3- and 6-mm cornea was quantified with coefficient of variance (CV). RESULTS: Both ET and CV significantly increased 1 month postoperatively (all P < .05). The stepwise multiple regression analysis showed that ET and CV were significantly correlated with preoperative ET and CV, respectively (all P < .01). The corrected spherical equivalent also significantly influenced ET and CV (all P < .01). Over the central 6-mm zone, the alterations of total corneal HOAs and individual Zernike components such as vertical coma (Z7) and spherical aberration (Z12, Z24) were significantly correlated with ET and CV (all P < .05). CONCLUSIONS: The SMILE-induced epithelial remodeling involved both ET and ET inhomogeneity. The modulation was associated with preoperative and treatment parameters, and exerted a significant impact on corneal HOA alterations especially over the central 6-mm cornea. Together with the amount of correction and corneal curvature gradient change, preoperative assessment of ET and ET inhomogeneity might help predict postoperative epithelial remodeling. [J Refract Surg. 2023;39(1):23-32.].