Differential isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 mutation-related landscape in intrahepatic cholangiocarcinoma.

BACKGROUND: Patients with intrahepatic cholangiocarcinoma (ICC) are prone to recurrence and poor survival. Targeted therapy related to isocitrate dehydrogenase (IDH) is an extremely important treatment. IDH1 and IDH2 mutations are generally thought to have similar effects on the tumor landscape. However, it is doubtful whether these 2 mutations have exactly the same effects on tumor cells and the tumor microenvironment. METHODS: All collected tumor samples were subjected to simultaneous whole-exon sequencing and proteome sequencing. RESULTS: IDH1 mutations accounted for 12.2%, and IDH2 mutations accounted for 5.5%, all missense mutations. Tumors with IDH mutations had lower proportions of KRAS and TP53 mutations. Mutated genes were obviously enriched in the kinase pathway in the tumors with IDH2 mutations. The signaling pathways were mainly enriched in the activation of cellular metabolic activities and an increase of inhibitory immune cells in the tumors with IDH mutations. Moreover, tumors had unique enrichment in DNA repair in IDH1 mutants and secretion of biological molecules in IDH2 mutants. Inhibitory immune cells might be more prominent in IDH2 mutants, and the expression of immune checkpoints PVR and HLA-DQB1 was more prominent in IDH1 mutants. IDH mutants were more related to metabolism-related and inflammation-immune response clusters, and some belonged to the DNA replication and repair cluster. CONCLUSIONS: These results revealed the differential IDH1 and IDH2 mutation-related landscapes, and we have provided an important reference database to guide ICC treatment.

Interlayer Structure Manipulation of FeOCl/MXene with Soft/Hard Interface Design for Safe Water Production Using Dechlorination Battery Deionization.

Suffering from the susceptibility to decomposition, the potential electrochemical application of FeOCl has greatly been hindered. The rational design of the soft-hard material interface can effectively address the challenge of stress concentration and thus decomposition that may occur in the electrodes during charging and discharging. Herein, interlayer structure manipulation of FeOCl/MXene using soft-hard interface design method were conducted for electrochemical dechlorination. FeOCl was encapsulated in Ti3C2Tx MXene nanosheets by electrostatic self-assembly layer by layer to form a soft-hard mechanical hierarchical structure, in which Ti3C2Tx was used as flexible buffer layers to relieve the huge volume change of FeOCl during Cl- intercalation/deintercalation and constructed a conductive network for fast charge transfer. The CDI dechlorination system of FeOCl/Ti3C2Tx delivered outstanding Cl- adsorption capacity (158.47 ± 6.98 mg g-1), rate (6.07 ± 0.35 mg g-1 min-1), and stability (over 94.49% in 30 cycles), and achieved considerable energy recovery (21.14 ± 0.25%). The superior dechlorination performance was proved to originate from the Fe2+/Fe3+ topochemical transformation and the deformation constraint effect of Ti3C2Tx on FeOCl. Our interfacial design strategy enables a hard-to-soft integration capacity, which can serve as a universal technology for solving the translational problem of electrode volume expansion.

Analysis of spatial and temporal changes in vegetation cover and its drivers in the Aksu River Basin, China.

Exploring vegetation dynamics in arid areas and their responses to different natural and anthropogenic factors is critical for understanding ecosystems. Based on the monthly MOD13Q1 (250 m) remote sensing data from 2000 to 2019, this study analyzed spatio-temporal changes in vegetation cover in the Aksu River Basin and predicted future change trends using one-dimensional linear regression, the Mann-Kendall test, and the Hurst index. Quantitative assessment of the magnitude of anthropogenic and natural drivers was performed using the Geodetector model. Eleven natural and anthropogenic factors were quantified and analyzed within five time periods. The influence of the driving factors on the changes in the normalized difference vegetation index (NDVI) in each period was calculated and analyzed. Four main results were found. (1) The overall vegetation cover in the region significantly grew from 2000 to 2019. The vegetation cover changes were dominated by expected future improvements, with a Hurst index average of 0.45. (2) Land use type, soil moisture, surface temperature, and potential vapor dispersion were the main drivers of NDVI changes, with annual average q-values above 0.2. (3) The driving effect of two-factor interactions was significantly greater than that of single factors, especially land use type interacts with other factors to a greater extent on vegetation cover. (4) The magnitude of the interaction between soil moisture and potential vapor dispersion and the magnitude of the interaction between anthropogenic factors and other factors showed an obvious increasing trend. Current soil moisture and human activities had a positive influence on the growth of vegetation in the area. The findings of this study are important for ecological monitoring and security as well as land desertification control.

Abnormal amygdala volume moderates parenting and anxiety symptoms in children and adolescents with anxiety disorder.

Anxiety disorders (AD) usually onset in childhood or adolescence and are related to brain development and early experiences during this period. As the hub of the fear circuit, the amygdala plays a crucial role in the development of emotional processing, and abnormalities in its structure and function are associated with anxiety disorders. We aim to uncover the amygdala volume's moderation between parenting and anxiety severity in children and adolescents with AD. 129 children and adolescents with anxiety and 135 age- and sex-matched Health controls (HC) using the publicly available Healthy Brain Network (HBN) dataset were included. Anxiety severity was measured using the Screen for Child Anxiety Related Disorders Self-report (SCARED-SR) and parenting was measured using the Alabama Parenting Questionnaire Self-Report (APQ-SR). We investigated age-related differences in amygdala volume in children and adolescents with anxiety disorders. Further, we examined the role of amygdala volume in moderating the association between parental involvement, particularly the maternal involvement, and anxiety symptoms in this population. We found larger bilateral amygdala in the AD group compared with the HC among the age range of 7-12. And increases in amygdala volume tended to negatively moderate the linear relationships between maternal involvement and anxiety symptoms in the AD group. These findings provide new evidence of abnormal brain alteration in children and adolescents with anxiety and may reflect proactive adaptations of adolescent brain development.

Characteristics and adsorption behavior of typical microplastics in long-term accelerated weathering simulation.

Microplastics can function as carriers in the environment, absorbing various toxins and spreading to diverse ecosystems. Toxins accumulated in microplastics have the potential to be re-released, posing a threat. In this study, two typical plastics, namely polyethylene (PE) and polystyrene (PS), along with the degradable plastic poly(butylene adipate-co-terephthalate) (PBAT), were subjected to a long-term ultraviolet alternating weathering experiment. The study investigated the variations in the weathering process and pollutant adsorption of microplastics of different particle sizes. Furthermore, the adsorption capacity of microplastics for various pollutants was assessed. The findings indicate that particle size significantly influences weathering, leading to variations in adsorption capacity. The weathered PE displays a higher adsorption capacity for azo dyes. Additionally, the adsorption capacity of PBAT for neutral red is double that of antibiotics. Importantly, the maximum adsorption capacity of PBAT for pollutants after aging is approximately 10 times greater than that of PE. Consequently, degradable plastics undergoing weathering in the natural environment may pose a higher ecological risk than traditional plastics.

S2VQ-VAE: Semi-Supervised Vector Quantised-Variational AutoEncoder for Automatic Evaluation of Trail Making Test.

BACKGROUND: Computer-aided detection of cognitive impairment garnered increasing attention, offering older adults in the community access to more objective, ecologically valid, and convenient cognitive assessments using multimodal sensing technology on digital devices. METHODOLOGY: In this study, we aimed to develop an automated method for screening cognitive impairment, building on paper- and electronic TMTs. We proposed a novel deep representation learning approach named Semi-Supervised Vector Quantised-Variational AutoEncoder (S2VQ-VAE). Within S2VQ-VAE, we incorporated intra- and inter-class correlation losses to disentangle class-related factors. These factors were then combined with various real-time obtainable features (including demographic, time-related, pressure-related, and jerk-related features) to create a robust feature engineering block. Finally, we identified the light gradient boosting machine as the optimal classifier. The experiments were conducted on a dataset collected from older adults in the community. RESULTS: The experimental results showed that the proposed multi-type feature fusion method outperformed the conventional method used in paper-based TMTs and the existing VAE-based feature extraction in terms of screening performance. CONCLUSIONS: In conclusion, the proposed deep representation learning method significantly enhances the cognitive diagnosis capabilities of behavior-based TMTs and streamlines large-scale community-based cognitive impairment screening while reducing the workload of professional healthcare staff.

Association between sociodemographics and change in alcohol or tobacco use behaviors during the COVID-19 pandemic.

OBJECTIVE: To examine the association between various sociodemographic factors with alcohol and tobacco use behaviors during the COVID-19 pandemic. METHODS: Participants from Ohio and Indiana were asked to participate in the 'Impact of COVID-19 on the Cancer Continuum Consortium' study (N = 32,989) from June-November 2020. Those who completed the survey and responded to key study questions were included (n = 5,374). Participants were asked about the frequency and type of alcohol and tobacco product used. Multivariable logistic regression was conducted to determine factors associated with the impact of COVID-19 on change in alcohol and/or tobacco use. RESULTS: Mean age was 57 years old, 68% were female, 90% non-Hispanic white, 75% married, and 31% lived in rural counties. Out of 5,374 participants, 53% used alcohol-only (n = 2,833), 5% used tobacco-only (n = 255), 7% used both alcohol and tobacco (n = 395), and 35% used neither alcohol nor tobacco (n = 1,891). Urban county of residence (vs. rural) was associated with an increase in alcohol-use (p = 0.0001), change in alcohol products (p = 0.023), and an increase in tobacco use (p = 0.05). Among alcohol-only users, those who were younger (OR = 0.97), female (OR = 1.58), married (OR = 1.69), of high socioeconomic status (OR = 1.99), residing in urban counties (OR = 1.65), and had elevated financial (OR = 1.06) and employment concerns (OR = 1.28) were significantly more likely to report increased alcohol-use. Similarly, among tobacco users, those who were younger (OR = 0.97), female (OR = 2.79), married (OR = 2.16) or divorced (OR = 2.83), and had higher levels of neighborhood disadvantage (OR = 2.19) were significantly more likely to report increased tobacco-use. CONCLUSIONS: Findings suggest targeted intervention and prevention strategies for young, female participants with elevated financial and employment concerns during the COVID-19 pandemic are necessary to mitigate risks associated with higher odds of alcohol and tobacco use. Our findings on alcohol and tobacco use may be a result of the unique social and economic influence of the pandemic on women.

No Effect of High Eating Frequency Compared with Low Eating Frequency on Appetite and Inflammation Biomarkers: Results from a Randomized Crossover Clinical Trial.

BACKGROUND: Eating frequency (EF) focuses on the total number of eating occasions per day and may influence metabolic health. OBJECTIVES: We sought to examine the effect of high compared with low EF on appetite regulation and inflammatory biomarkers among healthy adults. METHODS: Data are from a randomized, crossover trial (the Frequency of Eating and Satiety Hormones study). Participants (n = 50) completed 2 isocaloric 21-d study periods of low EF (3 eating occasions/d) and high EF (6 eating occasions/d) in random order with a 14-d washout period in between. Participants were free-living and consumed their own food, using study-directed, structured meal plans with identical foods and total energy in both study periods. On days 1 and 21 of each EF period, fasting blood was collected during in-person clinic visits to assess plasma concentrations of ghrelin, leptin, adiponectin, and high-sensitivity C-reactive protein (hs-CRP). Linear mixed models with EF, diet sequence, and period as fixed effects and participant as random effect were used to estimate the intervention effect. Interaction effects between EF and body fat percentage were examined. RESULTS: Among the 50 participants who completed the trial, 39 (78%) were women, 30 (60%) were Non-Hispanic White, and 40 (80%) had a body mass index of <25 kg/m2, and the mean age was 32.1 y. The differences between high and low EF in fasting ghrelin (geometric mean difference: 17.76 ng/mL; P = 0.60), leptin (geometric mean difference: 2.09 ng/mL; P = 0.14), adiponectin (geometric mean difference: 381.7 ng/mL; P = 0.32), and hs-CRP (geometric mean difference: -0.018 mg/dL; P = 0.08) were not statistically significant. No significant interaction was observed between EF and body fat percentage on appetite regulation and inflammatory biomarkers. CONCLUSIONS: No differences was observed in fasting ghrelin, leptin, adiponectin, and hs-CRP comparing high and low EF. Future studies are needed to understand the physiology of EF and appetite as they relate to metabolic health. This trial was registered at clinicaltrials.gov as NCT02392897.

ChemFH: an integrated tool for screening frequent false positives in chemical biology and drug discovery.

High-throughput screening rapidly tests an extensive array of chemical compounds to identify hit compounds for specific biological targets in drug discovery. However, false-positive results disrupt hit compound screening, leading to wastage of time and resources. To address this, we propose ChemFH, an integrated online platform facilitating rapid virtual evaluation of potential false positives, including colloidal aggregators, spectroscopic interference compounds, firefly luciferase inhibitors, chemical reactive compounds, promiscuous compounds, and other assay interferences. By leveraging a dataset containing 823 391 compounds, we constructed high-quality prediction models using multi-task directed message-passing network (DMPNN) architectures combining uncertainty estimation, yielding an average AUC value of 0.91. Furthermore, ChemFH incorporated 1441 representative alert substructures derived from the collected data and ten commonly used frequent hitter screening rules. ChemFH was validated with an external set of 75 compounds. Subsequently, the virtual screening capability of ChemFH was successfully confirmed through its application to five virtual screening libraries. Furthermore, ChemFH underwent additional validation on two natural products and FDA-approved drugs, yielding reliable and accurate results. ChemFH is a comprehensive, reliable, and computationally efficient screening pipeline that facilitates the identification of true positive results in assays, contributing to enhanced efficiency and success rates in drug discovery. ChemFH is freely available via https://chemfh.scbdd.com/.

Deficiency of CBL and CBLB ubiquitin ligases leads to hyper T follicular helper cell responses and lupus by reducing BCL6 degradation.

Recent evidence reveals hyper T follicular helper (Tfh) cell responses in systemic lupus erythematosus (SLE); however, molecular mechanisms responsible for hyper Tfh cell responses and whether they cause SLE are unclear. We found that SLE patients downregulated both ubiquitin ligases, casitas B-lineage lymphoma (CBL) and CBLB (CBLs), in CD4+ T cells. T cell-specific CBLs-deficient mice developed hyper Tfh cell responses and SLE, whereas blockade of Tfh cell development in the mutant mice was sufficient to prevent SLE. ICOS was upregulated in SLE Tfh cells, whose signaling increased BCL6 by attenuating BCL6 degradation via chaperone-mediated autophagy (CMA). Conversely, CBLs restrained BCL6 expression by ubiquitinating ICOS. Blockade of BCL6 degradation was sufficient to enhance Tfh cell responses. Thus, the compromised expression of CBLs is a prevalent risk trait shared by SLE patients and causative to hyper Tfh cell responses and SLE. The ICOS-CBLs axis may be a target to treat SLE.

Anti-inflammatory Effects of Bacteroidota Strains Derived From Outstanding Donors of Fecal Microbiota Transplantation for the Treatment of Ulcerative Colitis.

BACKGROUND: The proportion of certain Bacteroidota species decreased in patients with ulcerative colitis, and the recovery of Bacteroidota is associated with the efficacy of fecal microbiota transplantation therapy. We hypothesized that certain Bacteroidota may advance ulcerative colitis treatment. Accordingly, we aimed to evaluate the anti-inflammatory effects of Bacteroidota strains isolated from donors. METHODS: Donors with proven efficacy of fecal microbiota transplantation for ulcerative colitis were selected, and Bacteroidota strains were isolated from their stools. The immune function of Bacteroidota isolates was evaluated through in vitro and in vivo studies. RESULTS: Twenty-four Bacteroidota strains were isolated and identified. Using an in vitro interleukin (IL)-10 induction assay, we identified 4 Bacteroidota strains with remarkable IL-10-induction activity. Of these, an Alistipes putredinis strain exhibited anti-inflammatory effects in a mouse model of colitis induced by sodium dextran sulfate and oxazolone. However, 16S rRNA gene-based sequencing analysis of A. putredinis cultures in the in vivo study revealed unexpected Veillonella strain contamination. A second in vitro study confirmed that the coculture exhibited an even more potent IL-10-inducing activity. Furthermore, the production of A. putredinis-induced IL-10 was likely mediated via toll-like receptor 2 signaling. CONCLUSIONS: This study demonstrated that A. putredinis, a representative Bacteroidota species, exhibits anti-inflammatory effects in vivo and in vitro; however, the effects of other Bacteroidota species remain unexplored. Our fecal microbiota transplantation-based reverse translation approach using promising bacterial species may represent a breakthrough in microbiome drug development for controlling dysbiosis during ulcerative colitis.

We isolated Bacteroidota species from the feces of donors who were effectively cured of UC with fecal microbiota transplantation and proved the anti-inflammatory effects of Bacteroidota species, especially Alistipes putredinis, through cell experiments and in vivo experiments.

Deep learning and radiomics-based approach to meningioma grading: exploring the potential value of peritumoral edema regions.

Objective.To address the challenge of meningioma grading, this study aims to investigate the potential value of peritumoral edema (PTE) regions and proposes a unique approach that integrates radiomics and deep learning techniques.Approach.The primary focus is on developing a transfer learning-based meningioma feature extraction model (MFEM) that leverages both vision transformer (ViT) and convolutional neural network (CNN) architectures. Additionally, the study explores the significance of the PTE region in enhancing the grading process.Main results.The proposed method demonstrates excellent grading accuracy and robustness on a dataset of 98 meningioma patients. It achieves an accuracy of 92.86%, precision of 93.44%, sensitivity of 95%, and specificity of 89.47%.Significance.This study provides valuable insights into preoperative meningioma grading by introducing an innovative method that combines radiomics and deep learning techniques. The approach not only enhances accuracy but also reduces observer subjectivity, thereby contributing to improved clinical decision-making processes.

High-Level Disordered Metal-Organic Frameworks Synthesized by Interference-Oriented Attachment for Electrochemical Anion Sieve.

Disordered MOFs seamlessly amalgamate the robust stability and pore tunability inherent in crystalline MOFs with the advantages derived from abundant defects and active sites present in amorphous structures. This study pioneers the use of the interference-oriented attachment (IOA) mechanism to meticulously craft the morphology and crystal growth of MIL-101(Cr) (Cr-MOF), resulting in the successful synthesis of a high-level disordered Cr-MOF boasting an enhanced array of active sites and exceptional electrochemical properties. The correlation between disordered structures and the electrochemical properties of MOFs are elucidated using the lattice distortion index and fractal dimension. The high-level disordered MOF electrode showcases a remarkable fluoride sieving effect, outperforming conventional fluoride removal materials with a remarkable fluoride adsorption capacity of 41.04 mgNaF gelectrodes -1. First-principles calculations, in conjunction with relevant experiments, provided further validation that the disordered structure significantly enhances the defluorination performance of the material. This study introduces a novel approach for the direct bottom-up synthesis of high-level disordered MOFs, showcasing their potential for applications in electrochemical water treatment.

Harnessing Nanochaperone-Mediated Autophagy for Selective Clearance of Pathogenic Tau Protein in Alzheimer's Disease.

Accumulation of pathological tau is a hallmark of Alzheimer's disease (AD), which correlates more closely with cognitive impairment than does the amyloid-ß (Aß) burden. Autophagy is a powerful process for the clearance of toxic proteins including aberrant tau. However, compromised autophagy is demonstrated in neurodegeneration including AD, and current autophagy inducers remain enormously challenging due to inability of restoring autophagy pathway and lack of targeting specificity. Here, pathogenic tau-specific autophagy based on customized nanochaperone is developed for AD treatment. In this strategy, the nanochaperone can selectively bind to pathogenic tau and maintain tau homeostasis, thereby ensuring microtubule stability which is important for autophagy pathway. Meanwhile, the bound pathogenic tau can be sequestered in autophagosomes by in situ autophagy activation of nanochaperone. Consequently, autophagosomes wrapping with pathogenic tau are able to be trafficked along the stabilized microtubule to achieve successful fusion with lysosomes, resulting in the enhancement of autophagic flux and pathologic tau clearance. After treatment with this nanochaperone-mediated autophagy strategy, the tau burden, neuron damages, and cognitive deficits of AD mice are significantly alleviated in the brain. Therefore, this work represents a promising candidate for AD-targeted therapy and provides new insights into future design of anti-neurodegeneration drugs.

The impact of income level on skeletal muscle health in rural Chinese older residents: a study of mediating effects based on dietary knowledge.

China's rural residents have basically solved the problem of subsistence, but due to aging, the prevalence of sarcopenia (abbreviated as sarcopenia) has been increasing year by year, especially the skeletal muscle health of the rural older residents has not been sufficiently paid attention to, so analyses of the impact of income level on the skeletal muscle health of the older people in rural areas of China are of great practical significance. Based on the annual data of the China Health and Nutrition Survey (CHNS) in 2006, 2009, and 2011, we introduced the mediator variable of dietary knowledge and used the Probit model regression, mediation effect model, and instrumental variable regression to assess the skeletal muscle health status of the rural older people in China and explore the mechanism of the influence of the income level on the skeletal muscle health of the rural older residents in China. The primary objectives of this study were to evaluate the impact of income level on the skeletal muscle health status of older adults living in rural areas of China and to investigate the underlying mechanisms. By analyzing the findings of this study, our aim is to establish a correlation between the economic status and skeletal muscle health of older adults in rural communities, as well as elucidate the influence of income level and dietary knowledge on their skeletal muscle health. Through the attainment of these objectives, we hope to provide valuable insights and recommendations for enhancing skeletal muscle health among the rural older population in China. Based on our research findings, it can be inferred that there was a significant association between the financial status of rural older adults and their skeletal muscle health. Additionally, the prevalence of sarcopenia was lower among individuals with higher income levels, and there was a negative correlation between the prevalence of sarcopenia and the level of dietary knowledge among rural older individuals. The knowledge of dietary knowledge level of rural older people plays a mediating role in the income level and the prevalence of sarcopenia. Moreover, with the change in income level and the increase in age, the change in skeletal muscle health status showed obvious heterogeneity, in which the effect on the relatively younger (65-70 years old) samples was greater. Therefore, sustained income growth remains an effective way to improve the skeletal muscle health of older rural residents. At the same time, improving dietary knowledge and dietary quality among the older people is important in preventing a decline in muscle strength and physical function and in preventing the onset of sarcopenia.

ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization.

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.

Pallidum volume as a predictor for the effectiveness of mindfulness-based cognitive therapy and psycho-education in unmedicated patients with obsessive-compulsive disorder.

BACKGROUND: Mindfulness-based cognitive therapy (MBCT) has been documented to be effective in treating obsessive-compulsive disorder (OCD). However, the neurobiological basis of MBCT remains largely elusive, which makes it clinically challenging to predict which patients are more likely to respond poorly. Hence, identifying biomarkers for predicting treatment outcomes holds both scientific and clinical values. This prognostic study aims to investigate whether pre-treatment brain morphological metrics can predict the effectiveness of MBCT, compared with psycho-education (PE) as an active placebo, among patients with OCD. METHODS: A total of 32 patients with OCD were included in this prognostic study. They received magnetic resonance imaging (MRI) brain scans before treatment. Subsequently, 16 patients received 10 weeks of MBCT, while the other 16 patients underwent a 10-week PE program. The effectiveness of the treatments was primarily assessed by the reduction rate of the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) total score before and after the treatment. We investigated whether several predefined OCD-associated brain morphological metrics, selected based on prior published studies by the ENIGMA Consortium, could predict the treatment effectiveness. RESULTS: Both the MBCT and PE groups exhibited substantial reductions in Y-BOCS scores over 10 weeks of treatment, with the MBCT group showing a larger reduction. Notably, the pallidum total volume was associated with treatment effectiveness, irrespective of the intervention group. Specifically, a linear regression model utilizing the pre-treatment pallidum volume to predict the treatment effectiveness suggested that a one-cubic-centimeter increase in pallidum volume corresponded to a 22.3% decrease in the Y-BOCS total score reduction rate. CONCLUSIONS: Pallidum volume may serve as a promising predictor for the effectiveness of MBCT and PE, and perhaps, other treatments with the shared mechanisms by MBCT and PE, among patients with OCD.

CI-UNet: melding convnext and cross-dimensional attention for robust medical image segmentation.

Deep learning-based methods have recently shown great promise in medical image segmentation task. However, CNN-based frameworks struggle with inadequate long-range spatial dependency capture, whereas Transformers suffer from computational inefficiency and necessitate substantial volumes of labeled data for effective training. To tackle these issues, this paper introduces CI-UNet, a novel architecture that utilizes ConvNeXt as its encoder, amalgamating the computational efficiency and feature extraction capabilities. Moreover, an advanced attention mechanism is proposed to captures intricate cross-dimensional interactions and global context. Extensive experiments on two segmentation datasets, namely BCSD, and CT2USforKidneySeg, confirm the excellent performance of the proposed CI-UNet as compared to other segmentation methods.

Evaluating photodynamic therapy for oral precancerous lesions: Highlighting outcome measure of malignant transformation.

The main outcome measure assessed in previous studies on photodynamic therapy (PDT) for oral precancerous lesions (OPL) is clinical response based on the alteration in lesion size after treatment. However, the primary and secondary outcome measures of the interventions for OPL should be malignant transformation and recurrence. Thus, the objective of this short communication is to summarize the evidence on PDT in preventing the recurrence and malignant transformation of OPL. There were 16 eligible studies which addressed the issue of OPL patients who received PDT with recurrence outcome, and the pooled recurrence rate (95% confidence interval) was analyzed to be 20.1% (16.2-24.6%). Notably, only 1 study reported that 7.5% of malignant transformation rate for OPL received PDT. These should be interpreted with caution due to low-level evidence, such as differences in study design, clinical and pathological features of patients enrolled, limited sample size, short follow-up time. Given few evaluated the effect of PDT on malignant transformation, we highlight that this primary outcome measure of OPL needs to be investigated in further well-designed longitudinal studies with adequate follow-up periods.