Prediction of mechanical ventilation in Guillain-Barré syndrome at admission: Construction of a nomogram and comparison with the EGRIS model.

Background: Respiratory failure requiring mechanical ventilation (MV) is a common and severe complication of Guillain-Barré syndrome (GBS) with a reported incidence ranging from 20 % to 30 %. Thus, we aim to develop a nomogram to evaluate the risk of MV in patients with GBS at admission and tailor individualized care and treatment. Methods: A total of 633 patients with GBS (434 in the training set, and 199 in the validation set) admitted to the First Hospital of Jilin University, Changchun, China from January 2010 to January 2021 were retrospectively enrolled. Subjects (n = 71) from the same institution from January 2021 to May 2022 were prospectively collected and allocated to the testing set. Multivariable logistic regression analysis was applied to build a predictive model incorporating the optimal features selected in the least absolute shrinkage and selection operator (LASSO) in the training set. The predictive model was validated using internal bootstrap resampling, an external validation set, and a prospective testing set, and the model's performance was assessed by using the concordance index (C-index), calibration curves, and decision curve analysis (DCA). Finally, we established a multivariable logistic model by using variables of the Erasmus GBS Respiratory Insufficiency Score (EGRIS) and did the same analysis to compare the performance of our predictive model with the EGRIS model. Results: Variables in the final model selected by LASSO included time from onset to admission, facial and/or bulbar weakness, Medical Research Council sum score at admission, neutrophil-to-lymphocyte ratio, and platelet-lymphocyte ratio. The model presented as a nomogram displaying favorable discriminative ability with a C-index of 0.914 in the training set, 0.903 in the internal validation set, 0.953 in the external validation set, and 0.929 in the testing set. The model was well-calibrated and clinically useful as assessed by the calibration curve and DCA. As compared with the EGRIS model, our predictive model displayed satisfactory performance. Conclusions: We constructed a nomogram for early prediction of the risk of MV in patients with GBS. This model had satisfactory performance and appeared more efficient than the EGRIS model in Chinese patients with GBS.

Astrocyte-derived CHI3L1 signaling impairs neurogenesis and cognition in the demyelinated hippocampus.

Cognitive dysfunction is a feature in multiple sclerosis (MS), a chronic inflammatory demyelinating disorder. A notable aspect of MS brains is hippocampal demyelination, which is closely associated with cognitive decline. However, the mechanisms underlying this phenomenon remain unclear. Chitinase-3-like (CHI3L1), secreted by activated astrocytes, has been identified as a biomarker for MS progression. Our study investigates CHI3L1's function within the demyelinating hippocampus and demonstrates a correlation between CHI3L1 expression and cognitive impairment in patients with MS. Activated astrocytes release CHI3L1 in reaction to induced demyelination, which adversely affects the proliferation and differentiation of neural stem cells and impairs dendritic growth, complexity, and spine formation in neurons. Our findings indicate that the astrocytic deletion of CHI3L1 can mitigate neurogenic deficits and cognitive dysfunction. We showed that CHI3L1 interacts with CRTH2/receptor for advanced glycation end (RAGE) by attenuating ß-catenin signaling. The reactivation of ß-catenin signaling can revitalize neurogenesis, which holds promise for therapy of inflammatory demyelination.

Current advances of chlorinated organics degradation by bioelectrochemical systems: a review.

Chlorinated organic compounds (COCs) are typical refractory organic compounds, having high biological toxicity. These compounds are a type of pervasive pollutants that can be present in polluted soil, air, and various types of waterways, such as groundwater, rivers, and lakes, posing a significant threat to the ecological environment and human health. Bioelectrochemical systems (BESs) are an effective strategy for the degradation of bio-refractory compounds. BESs improve the waste treatment efficiency through the application of weak electrical stimulation. This review discusses the processes of BESs configurations and degradation performances in different environmental media including wastewater, soil, waste gas and groundwater. In addition, the degradation mechanisms and performance-enhancing additives are summarized. The future challenges and perspectives on the development of BES for COCs removal are briefly discussed.

Psychosocial adjustment changes and related factors in young and middle-aged patients with first-episode acute myocardial infarction: a longitudinal study.

AIMS: This study aimed to explore the change trend and group heterogeneity of psychosocial adjustment level and to determine its influencing factors among young and middle-aged patients with first-episode acute myocardial infarction (AMI). METHODS AND RESULTS: The Psychosocial Adjustment Scale of Illness was used to assess the psychosocial adjustment level of the patients at 1, 3, and 6 months after discharge, respectively. Data were analyzed using Pearson correlation analysis, generalized estimating equations, and growth mixed models. A total of 233 patients were included, and their psychosocial adjustment scores at the three-time points were 57.18 ± 15.50, 36.17 ± 15.02, and 24.22 ± 12.98, respectively. The trajectories of changes in patients' psychosocial adjustment levels were divided into three latent categories: moderate adjustment improvement group (72.5%), low adjustment improvement group (16.3%), and persistent maladjustment group (11.2%). Among them, predictors of the persistent maladjustment group included no spouse, low monthly family income per capita, normal body mass index, never smoking, never exercising, combined with hyperlipidemia, low social support, submission coping, and high perceived stress. CONCLUSIONS: The psychosocial adjustment level of young and middle-aged patients with first-episode AMI showed an upward trend within 6 months after discharge, and there was group heterogeneity in the change trajectory of psychosocial adjustment level. It is suggested that a multi-center, large-sample longitudinal study should be carried out in the future, and the time of follow-up investigation should be extended to further clarify the change trajectory and influencing factors of psychosocial adjustment of patients with different subtypes, to provide the theoretical basis for formulating targeted intervention programs.

A multicenter, randomized, open-label, phase 2 clinical study of telitacicept in adult patients with generalized myasthenia gravis.

BACKGROUND AND PURPOSE: This study aimed to investigate the clinical efficacy and safety of telitacicept in patients with generalized myasthenia gravis (gMG) who tested positive for acetylcholine receptor antibodies or muscle-specific kinase antibodies and were receiving standard-of-care therapy. METHODS: Patients meeting the eligibility criteria were randomly assigned to receive telitacicept subcutaneously once a week for 24 weeks in addition to standard-of-care treatment. The primary efficacy endpoint was the mean change in the quantitative myasthenia gravis (QMG) score from baseline to week 24. Secondary efficacy endpoints included mean change in QMG score from baseline to week 12 and gMG clinical absolute score from baseline to week 24. Additionally, safety, tolerability and pharmacodynamics were assessed. RESULTS: Twenty-nine of the 41 patients screened were randomly selected and enrolled. The mean (± standard deviation [SD]) reduction in QMG score from baseline to week 24 was 7.7 (± 5.34) and 9.6 (± 4.29) in the 160 mg and 240 mg groups, respectively. At week 12, mean reductions in QMG scores for these two groups were 5.8 (± 5.85) and 9.5 (± 5.03), respectively, indicating rapid clinical improvement. Safety analysis revealed no adverse events leading to discontinuation or mortalities. All patients showed consistent reductions in serum immunoglobulin (Ig) A, IgG and IgM levels throughout the study. CONCLUSION: Telitacicept demonstrated safety, good tolerability and reduced clinical severity throughout the study period. Further validation of the clinical efficacy of telitacicept in gMG will be conducted in an upcoming phase 3 clinical trial.

Lycium barbarum glycopeptide ameliorates motor and visual deficits in autoimmune inflammatory diseases.

BACKGROUND: Lycium barbarum glycopeptide (LbGp), extracted from the traditional Chinese medicine (TCM) of Lycium barbarum (LB), provides a neuroprotective effect against neurodegenerative and neuroimmune disorders contributing to its immunomodulatory and anti-inflammatory roles. Neuromyelitis optica spectrum disorders (NMOSD) is an autoimmune-mediated central nervous system (CNS) demyelinating disease, clinically manifested as transverse myelitis (TM) and optic neuritis. However, no drug has been demonstrated to be effective in relieving limb weakness and visual impairment of NMOSD patients. PURPOSE: This study investigates the potential role of LbGp in ameliorating pathologic lesions and improving neurological dysfunction during NMOSD progression, and to elucidate the underlying mechanisms for the first time. STUDY DESIGN: We administrate LbGp in experimental NMOSD models in ex vivo and in vivo to explore its effect on NMOSD. METHODS: To evaluate motor function, both rotarod and gait tasks were performed in systemic NMOSD mice models. Furthermore, we assessed the severity of NMO-like lesions of astrocytes, organotypic cerebellar slices, as well as brain, spinal cord and optic nerve sections from NMOSD mouse models with LbGp treatment by immunofluorescent staining. In addition, demyelination levels in optic nerve were measured by G-ratio through Electro-microscopy (EM). And inflammation response was explored through detecting the protein levels of proinflammatory cytokines and NF-κB signaling in astrocytic culture medium and spinal cord homogenates respectively by Elisa and by Western blotting. RESULTS: LbGp could significantly reduce astrocytes injury, demyelination, and microglial activation in NMOSD models. In addition, LbGp also improved locomotor and visual dysfunction through preventing neuron and retinal ganglion cells (RGCs) from inflammatory attack in a systemic mouse model. Mechanistically, LbGp inhibits proinflammatory factors release via inhibition of NF-κB signaling in NMOSD models. CONCLUSION: This study provides evidence to develop LbGp as a functional TCM for the clinical treatment of NMOSD.

Discovery of Small Molecule Interleukin 17A Inhibitors with Novel Binding Mode and Stoichiometry: Optimization of DNA-Encoded Chemical Library Hits to In Vivo Active Compounds.

Dysregulation of IL17A drives numerous inflammatory and autoimmune disorders with inhibition of IL17A using antibodies proven as an effective treatment. Oral anti-IL17 therapies are an attractive alternative option, and several preclinical small molecule IL17 inhibitors have previously been described. Herein, we report the discovery of a novel class of small molecule IL17A inhibitors, identified via a DNA-encoded chemical library screen, and their subsequent optimization to provide in vivo efficacious inhibitors. These new protein-protein interaction (PPI) inhibitors bind in a previously undescribed mode in the IL17A protein with two copies binding symmetrically to the central cavities of the IL17A homodimer.

Connectome-driven neural inventory of a complete visual system.

Vision provides animals with detailed information about their surroundings, conveying diverse features such as color, form, and movement across the visual scene. Computing these parallel spatial features requires a large and diverse network of neurons, such that in animals as distant as flies and humans, visual regions comprise half the brain's volume. These visual brain regions often reveal remarkable structure-function relationships, with neurons organized along spatial maps with shapes that directly relate to their roles in visual processing. To unravel the stunning diversity of a complex visual system, a careful mapping of the neural architecture matched to tools for targeted exploration of that circuitry is essential. Here, we report a new connectome of the right optic lobe from a male Drosophila central nervous system FIB-SEM volume and a comprehensive inventory of the fly's visual neurons. We developed a computational framework to quantify the anatomy of visual neurons, establishing a basis for interpreting how their shapes relate to spatial vision. By integrating this analysis with connectivity information, neurotransmitter identity, and expert curation, we classified the ~53,000 neurons into 727 types, about half of which are systematically described and named for the first time. Finally, we share an extensive collection of split-GAL4 lines matched to our neuron type catalog. Together, this comprehensive set of tools and data unlock new possibilities for systematic investigations of vision in Drosophila, a foundation for a deeper understanding of sensory processing.

POLE -related gene signature predicts prognosis, immune feature, and drug therapy in human endometrioid carcinoma.

The POLE subtype of Endometrial carcinoma (EC) is linked to a favourable prognosis in the molecular classification. We proposed to ascertain the potential connection between the POLE subtype and improved prognosis. In order to forecast the prognosis, least absolute shrinkage and selection operator (LASSO) Cox regression analysis and weighted gene co-expression network analysis (WGCNA) were employed, and a POLE-related risk signature (PRS) model was developed and validated. Single-sample gene set enrichment analysis (ssGSEA) with the "GSVA" package was employed to analyse immunity characteristics. Drug susceptibility studies were conducted to compare the half-maximal inhibitory concentration (IC50) of medicines between high- and low-risk groups. The PRS model was generated employing the LASSO Cox regression coefficients of the ELF1, MMADHC, andAL021707.6 genes. Our study demonstrated that the risk score was linked to tumour stage, grade, and survival. Furthermore, the low-risk group possessed elevated levels of gene expression connected with immunological checkpoints and HLA. Our outcomes emerged that the PRS model might have value in identifying patients with a good prognosis and in facilitating personalised treatment in the clinic.

Cardiopulmonary Coupling Spectrogram as an Ambulatory Method for Assessing Sleep Disorders in Patients With Autoimmune Encephalitis.

BACKGROUND AND OBJECTIVES: Sleep disorders are a common and important clinical feature in patients with autoimmune encephalitis (AE); however, they are poorly understood. We aimed to evaluate whether cardiopulmonary coupling (CPC), an electrocardiogram-based portable sleep monitoring technology, can be used to assess sleep disorders in patients with AE. METHODS: Patients fulfilling the diagnostic criteria of AE were age- and sex-matched with recruited healthy control subjects. All patients and subjects received CPC testing between August 2020 and December 2022. Demographic data, clinical information, and Pittsburgh Sleep Quality Index (PSQI) scores were collected from the medical records. Data analysis was performed using R language programming software. RESULTS: There were 60 patients with AE (age 26.0 [19.8-37.5] years, male 55%) and 66 healthy control subjects (age 30.0 [25.8-32.0] years, male 53%) included in this study. Compared with healthy subjects, patients with AE had higher PSQI scores (7.00 [6.00-8.00] vs 3.00 [2.00-4.00], p < 0.001), lower sleep efficiency (SE 80% [71%-87%] vs 92% [84%-95%], p < 0.001), lower percentage of high-frequency coupling (25% [14%-43%] vs 45% [38%-53%], p < 0.001), higher percentage of REM sleep (19% ± 9% vs 15% ± 7%, p < 0.001), higher percentage of wakefulness (W% 16% [11%-25%] vs 8% [5%-16%], p = 0.074), higher low-frequency to high-frequency ratio (LF/HF 1.29 [0.82-2.40] vs 0.91 [0.67-1.29], p = 0.001), and a higher CPC-derived respiratory disturbance index (9.78 [0.50-22.2] vs 2.95 [0.40-6.53], p < 0.001). Follow-up evaluation of 14 patients showed a decrease in the PSQI score (8.00 [6.00-9.00] vs 6.00 [5.00-7.00], p = 0.008), an increased SE (79% [69%-86%] vs 89% [76%-91%], p = 0.030), and a decreased W% (20% [11%-30%] vs 11% [8%-24], p = 0.035). Multiple linear regression indicated that SE (-7.49 [-9.77 to -5.21], p < 0.001) and LF/HF ratio (0.37 [0.13-0.6], p = 0.004) were independent factors affecting PSQI scores in patients with AE. DISCUSSION: Sleep disorders with autonomic dysfunction are common in patients with AE. Improvements in the PSQI score and SE precede the restoration of sleep microstructural disruption in the remission stage. CPC parameters may be useful in predicting sleep disorders in patients with AE.

Genome-Wide Association Study Identifies IFIH1 and HLA-DQB1*05:02 Loci Associated With Anti-NMDAR Encephalitis.

BACKGROUND AND OBJECTIVES: Anti-N-methyl-d-aspartate receptor (NMDAR) encephalitis is a rare autoimmune neurologic disorder, the genetic etiology of which remains poorly understood. Our study aims to investigate the genetic basis of this disease in the Chinese Han population. METHODS: We performed a genome-wide association study and fine-mapping study within the major histocompatibility complex (MHC) region of 413 Chinese patients with anti-NMDAR encephalitis recruited from 6 large tertiary hospitals and 7,127 healthy controls. RESULTS: Our genome-wide association analysis identified a strong association at the IFIH1 locus on chromosome 2q24.2 (rs3747517, p = 1.06 × 10-8, OR = 1.55, 95% CI, 1.34-1.80), outside of the human leukocyte antigen (HLA) region. Furthermore, through a fine-mapping study of the MHC region, we discovered associations for 3 specific HLA class I and II alleles. Notably, HLA-DQB1*05:02 (p = 1.43 × 10-12; OR, 2.10; 95% CI 1.70-2.59) demonstrates the strongest association among classical HLA alleles, closely followed by HLA-A*11:01 (p = 4.36 × 10-7; OR, 1.52; 95% CI 1.29-1.79) and HLA-A*02:07 (p = 1.28 × 10-8; OR, 1.87; 95% CI 1.50-2.31). In addition, we uncovered 2 main HLA amino acid variation associated with anti-NMDAR encephalitis including HLA-DQß1-126H (p = 1.43 × 10-12; OR, 2.10; 95% CI 1.70-2.59), exhibiting a predisposing effect, and HLA-B-97R (p = 3.40 × 10-8; OR, 0.63; 95% CI 0.53-0.74), conferring a protective effect. Computational docking analysis suggested a close relationship between the NR1 subunit of NMDAR and DQB1*05:02. DISCUSSION: Our findings indicate that genetic variation in IFIH1, involved in the type I interferon signaling pathway and innate immunity, along with variations in the HLA class I and class II genes, has substantial implications for the susceptibility to anti-NMDAR encephalitis in the Chinese Han population.

A deep profile of gene expression across 18 human cancers.

Clinically and biologically valuable information may reside untapped in large cancer gene expression data sets. Deep unsupervised learning has the potential to extract this information with unprecedented efficacy but has thus far been hampered by a lack of biological interpretability and robustness. Here, we present DeepProfile, a comprehensive framework that addresses current challenges in applying unsupervised deep learning to gene expression profiles. We use DeepProfile to learn low-dimensional latent spaces for 18 human cancers from 50,211 transcriptomes. DeepProfile outperforms existing dimensionality reduction methods with respect to biological interpretability. Using DeepProfile interpretability methods, we show that genes that are universally important in defining the latent spaces across all cancer types control immune cell activation, while cancer type-specific genes and pathways define molecular disease subtypes. By linking DeepProfile latent variables to secondary tumor characteristics, we discover that tumor mutation burden is closely associated with the expression of cell cycle-related genes. DNA mismatch repair and MHC class II antigen presentation pathway expression, on the other hand, are consistently associated with patient survival. We validate these results through Kaplan-Meier analyses and nominate tumor-associated macrophages as an important source of survival-correlated MHC class II transcripts. Our results illustrate the power of unsupervised deep learning for discovery of novel cancer biology from existing gene expression data.

Current status of superoxide dismutase 2 on oral disease progression by supervision of ROS.

The recent Global Burden of Disease results have demonstrated that oral diseases are some of the most significant public health challenges facing the world. Owing to its specific localization advantage, superoxide dismutase 2 (SOD2 or MnSOD) has the ability to process the reactive oxygen species (ROS) produced by mitochondrial respiration before anything else, thereby impacting the occurrence and development of diseases. In this review, we summarize the processes of common oral diseases in which SOD2 is involved. SOD2 is upregulated in periodontitis to protect the tissue from the distant damage caused by excessive ROS and further reduce inflammatory progression. SOD2 also participates in the specific pathogenesis of oral cancers and dental diseases. The clinical application prospects of SOD2 in oral diseases will be discussed further, referencing the differences and relationship between oral diseases and other clinical systemic diseases.

Combination of serum markers with optical coherence tomography angiography for evaluating neuromyelitis optica spectrum disorders and multiple sclerosis.

BACKGROUND: Neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS), autoimmune inflammatory diseases of the central nervous system, affect the optic nerve and brain. A lumbar puncture to obtain biomarkers is highly invasive. Serum biomarkers and optical coherence tomography angiography (OCTA) are more accessible and less expensive than magnetic resonance imaging and provide reliable, reproducible measures of neuroaxonal damage. This study investigated the association between serum neurofilament light chain (sNfL), serum glial fibrillary acidic protein (sGFAP), and OCTA metrics. Serum sNfL and sGFAP levels, OCTA values, and clinical characteristics were compared among 91 patients with NMOSD, 81 patients with MS, and 34 healthy controls (HCs) at baseline and 1-year follow-up. RESULTS: sNfL and sGFAP levels were higher while the sGFAP/sNfL quotients were significantly lower in NMOSD and MS patients than those in HCs. At baseline, the average thicknesses of the peripapillary retinal nerve fibre layer (pRNFL) and macular ganglion cell-inner plexiform layer (mGC-IPL) were significantly smaller in NMOSD and MS patients than those in HCs (pRNFL: MS 92.0 [80.2; 101] µm, NMOSD 80.0 [59.0; 95.8] µm, vs HC 99.0 [92.0; 104] µm, p < 0.001; mGC-IPL: MS 74.5 [64.2; 81.0] µm, NMOSD 68.0 [56.0; 81.0] µm, vs HC 83.5 [78.0; 88.0] µm, p < 0.001). The vessel density (VD) and perfusion density (PD) were increased in MS patients without optic neuritis compared to HCs (VD: MS 16.7 [15.6; 17.9] HC 15.3 [13.4; 16.9], p = 0.008; PD: MS 0.41 [0.38; 0.43], HC 0.37 [0.32; 0.41], p = 0.017). In NMOSD patients without optic neuritis, sNfL was significantly associated with PD at baseline (r = 0.329, q = 0.041). The baseline and follow-up values of the sNfL level and average pRNFL and mGC-IPL thicknesses in MS patients showed significant differences. NMOSD patients showed significant differences between baseline and follow-up sNfL and sGFAP levels but not OCTA metrics. CONCLUSION: Changes in retinal microvasculature might occur earlier than those in retinal structure and may therefore serve as a promising diagnostic marker for early NMOSD. The combination of serum markers and OCTA metrics could be used to evaluate and differentiate between MS and NMOSD.

Minimising the payload solvent exposed hydrophobic surface area optimises the antibody-drug conjugate properties.

Glucocorticoid receptor modulators (GRMs) are an established and successful compound class for the treatment of multiple diseases. In addition, they are an area of high interest as payloads for antibody-drug conjugate s(ADCs) in both immunology and oncology. Solving the crystal structure of compound 2, the GRM payload from ABBV-3373 and ABBV-154, in the ligand binding domain of the glucocorticoid receptor (GR) revealed key information to facilitate optimal ADC payload design. All four critical H-bonds between the oxygen functional groups on the hexadecahydro-1H-cyclopenta[a]phenanthrene ring system of the small molecule and protein were shown to be made (carbonyl at C3 to Gln570 and Arg611 and Asn564, carbonyl at C20 to Thr739, hydroxyl at C21 to Asn 564 and Thr739). In addition, an extra H-bond between the linker attachment site on compound 2, the aniline in the biaryl region, was observed. Confirmation of the stereochemistry of the acetal in compound 2 as (R) was established. Finally, the importance of minimising the exposed hydrophobic surface area of a payload to reduce the negative impact on the properties of resulting ADCs, like aggregation, was rationalised by comparison of (R)-acetal compound 2 and (S)-acetal compound 3.

An anti-TNF-glucocorticoid receptor modulator antibody-drug conjugate is efficacious against immune-mediated inflammatory diseases.

Glucocorticoids (GCs) are efficacious drugs used for treating many inflammatory diseases, but the dose and duration of administration are limited because of severe side effects. We therefore sought to identify an approach to selectively target GCs to inflamed tissue. Previous work identified that anti-tumor necrosis factor (TNF) antibodies that bind to transmembrane TNF undergo internalization; therefore, an anti-TNF antibody-drug conjugate (ADC) would be mechanistically similar, where lysosomal catabolism could release a GC receptor modulator (GRM) payload to dampen immune cell activity. Consequently, we have generated an anti-TNF-GRM ADC with the aim of inhibiting pro-inflammatory cytokine production from stimulated human immune cells. In an acute mouse model of contact hypersensitivity, a murine surrogate anti-TNF-GRM ADC inhibited inflammatory responses with minimal effect on systemic GC biomarkers. In addition, in a mouse model of collagen-induced arthritis, single-dose administration of the ADC, delivered at disease onset, was able to completely inhibit arthritis for greater than 30 days, whereas an anti-TNF monoclonal antibody only partially inhibited disease. ADC treatment at the peak of disease was also able to attenuate the arthritic phenotype. Clinical data for a human anti-TNF-GRM ADC (ABBV-3373) from a single ascending dose phase 1 study in healthy volunteers demonstrated antibody-like pharmacokinetic profiles and a lack of impact on serum cortisol concentrations at predicted therapeutic doses. These data suggest that an anti-TNF-GRM ADC may provide improved efficacy beyond anti-TNF alone in immune mediated diseases while minimizing systemic side effects associated with standard GC treatment.

Constantly optimized mean teacher for semi-supervised 3D MRI image segmentation.

The mean teacher model and its variants, as important methods in semi-supervised learning, have demonstrated promising performance in magnetic resonance imaging (MRI) data segmentation. However, the superior performance of teacher model through exponential moving average (EMA) is limited by the unreliability of unlabeled image, resulting in potentially unreliable predictions. In this paper, we propose a framework to optimized the teacher model with reliable expert-annotated data while preserving the advantages of EMA. To avoid the tight coupling that results from EMA, we leverage data augmentations to provide two distinct perspectives for the teacher and student models. The teacher model adopts weak data augmentation to provide supervision for the student model and optimizes itself with real annotations, while the student uses strong data augmentation to avoid overfitting on noise information. In addition, double softmax helps the model resist noise and continue learning meaningful information from the images, which is a key component in the proposed model. Extensive experiments show that the proposed method exhibits competitive performance on the Left Atrium segmentation MRI dataset (LA) and the Brain Tumor Segmentation MRI dataset (BraTS2019). For the LA dataset, we achieved a dice of 91.02% using only 20% labeled data, which is close to the dice of 91.14% obtained by the supervised approach using 100% labeled data. For the BraTs2019 dataset, the proposed method achieved 1.02% and 1.92% improvement on 5% and 10% labeled data, respectively, compared to the best baseline method on this dataset. This study demonstrates that the proposed model can be a potential candidate for medical image segmentation in semi-supervised learning scenario.

Correction: Minimising the payload solvent exposed hydrophobic surface area optimises the antibody-drug conjugate properties.

[This corrects the article DOI: 10.1039/D3MD00540B.].

Inhibition of MALT1 and BCL2 induces synergistic anti-tumor activity in models of B cell lymphoma.

The activated B cell (ABC) subset of diffuse large B cell lymphoma (DLBCL) is characterized by chronic B cell receptor signaling and associated with poor outcomes when treated with standard therapy. In ABC-DLBCL, MALT1 is a core enzyme that is constitutively activated by stimulation of the B cell receptor or gain-of-function mutations in upstream components of the signaling pathway, making it an attractive therapeutic target. We discovered a novel small molecule inhibitor, ABBV-MALT1, that potently shuts down B cell signaling selectively in ABC-DLBCL preclinical models leading to potent cell growth and xenograft inhibition. We also identified a rational combination partner for ABBV-MALT1 in the BCL2 inhibitor, venetoclax, which when combined significantly synergizes to elicit deep and durable responses in preclinical models. This work highlights the potential of ABBV-MALT1 monotherapy and combination with venetoclax as effective treatment options for patients with ABC-DLBCL.