Tas2R activation relaxes airway smooth muscle by release of Gαt targeting on AChR signaling.

Both chronic obstructive pulmonary disease (COPD) and asthma are severe respiratory diseases. Bitter receptor-mediated bronchodilation is a potential therapy for asthma, but the mechanism underlying the agonistic relaxation of airway smooth muscle (ASM) is not well defined. By exploring the ASM relaxation mechanism of bitter substances, we observed that pretreatment with the bitter substances nearly abolished the methacholine (MCh)-induced increase in the ASM cell (ASMC) calcium concentration, thereby suppressing the calcium-induced contraction release. The ASM relaxation was significantly inhibited by simultaneous deletion of three Gαt proteins, suggesting an interaction between Tas2R and AChR signaling cascades in the relaxation process. Biochemically, the Gαt released by Tas2R activation complexes with AChR and blocks the Gαq cycling of AChR signal transduction. More importantly, a bitter substance, kudinoside A, not only attenuates airway constriction but also significantly inhibits pulmonary inflammation and tissue remodeling in COPD rats, indicating its modulation of additional Gαq-associated pathological processes. Thus, our results suggest that Tas2R activation may be an ideal strategy for halting multiple pathological processes of COPD.

Deep learning in preclinical antibody drug discovery and development.

Antibody drugs have become a key part of biotherapeutics. Patients suffering from various diseases have benefited from antibody therapies. However, its development process is rather long, expensive and risky. To speed up the process, reduce cost and improve success rate, artificial intelligence, especially deep learning methods, have been widely used in all aspects of preclinical antibody drug development, from library generation to hit identification, developability screening, lead selection and optimization. In this review, we systematically summarize antibody encodings, deep learning architectures and models used in preclinical antibody drug discovery and development. We also critically discuss challenges and opportunities, problems and possible solutions, current applications and future directions of deep learning in antibody drug development.

MU variability in CBCT-guided online adaptive radiation therapy.

PURPOSE: CBCT-guided online-adaptive radiotherapy (oART) systems have been made possible by using artificial intelligence and automation to substantially reduce treatment planning time during on-couch adaptive sessions. Evaluating plans generated during an adaptive session presents significant challenges to the clinical team as the planning process gets compressed into a shorter window than offline planning. We identified MU variations up to 30% difference between the adaptive plan and the reference plan in several oART sessions that caused the clinical team to question the accuracy of the oART dose calculation. We investigated the cause of MU variation and the overall accuracy of the dose delivered when MU variations appear unnecessarily large. METHODS: Dosimetric and adaptive plan data from 604 adaptive sessions of 19 patients undergoing CBCT-guided oART were collected. The analysis included total MU per fraction, planning target volume (PTV) and organs at risk (OAR) volumes, changes in PTV-OAR overlap, and DVH curves. Sessions with MU greater than two standard deviations from the mean were reoptimized offline, verified by an independent calculation system, and measured using a detector array. RESULTS: MU variations relative to the reference plan were normally distributed with a mean of -1.0% and a standard deviation of 11.0%. No significant correlation was found between MU variation and anatomic changes. Offline reoptimization did not reliably reproduce either reference or on-couch total MUs, suggesting that stochastic effects within the oART optimizer are likely causing the variations. Independent dose calculation and detector array measurements resulted in acceptable agreement with the planned dose. CONCLUSIONS: MU variations observed between oART plans were not caused by any errors within the oART workflow. Providers should refrain from using MU variability as a way to express their confidence in the treatment planning accuracy. Clinical decisions during on-couch adaptive sessions should rely on validated secondary dose calculations to ensure optimal plan selection.

The thymus regulates skeletal muscle regeneration by directly promoting satellite cell expansion.

The thymus is the central immune organ, but it is known to progressively degenerate with age. As thymus degeneration is paralleled by the wasting of aging skeletal muscle, we speculated that the thymus may play a role in muscle wasting. Here, using thymectomized mice, we show that the thymus is necessary for skeletal muscle regeneration, a process tightly associated with muscle aging. Compared to control mice, the thymectomized mice displayed comparable growth of muscle mass, but decreased muscle regeneration in response to injury, as evidenced by small and sparse regenerative myofibers along with inhibited expression of regeneration-associated genes myh3, myod, and myogenin. Using paired box 7 (Pax7)-immunofluorescence staining and 5-Bromo-2'-deoxyuridine-incorporation assay, we determined that the decreased regeneration capacity was caused by a limited satellite cell pool. Interestingly, the conditioned culture medium of isolated thymocytes had a potent capacity to directly stimulate satellite cell expansion in vitro. These expanded cells were enriched in subpopulations of quiescent satellite cells (Pax7highMyoDlowEdUpos) and activated satellite cells (Pax7highMyoDhighEdUpos), which were efficiently incorporated into the regenerative myofibers. We thus propose that the thymus plays an essential role in muscle regeneration by directly promoting satellite cell expansion and may function profoundly in the muscle aging process.

Novel biallelic mutations in TMEM126B cause splicing defects and lead to Leigh-like syndrome with severe complex I deficiency.

Leigh syndrome (LS)/Leigh-like syndrome (LLS) is one of the most common mitochondrial disease subtypes, caused by mutations in either the nuclear or mitochondrial genomes. Here, we identified a novel intronic mutation (c.82-2 A > G) and a novel exonic insertion mutation (c.290dupT) in TMEM126B from a Chinese patient with clinical manifestations of LLS. In silico predictions, minigene splicing assays and patients' RNA analyses determined that the c.82-2 A > G mutation resulted in complete exon 2 skipping, and the c.290dupT mutation provoked partial and complete exon 3 skipping, leading to translational frameshifts and premature termination. Functional analysis revealed the impaired mitochondrial function in patient-derived lymphocytes due to severe complex I content and assembly defect. Altogether, this is the first report of LLS in a patient carrying mutations in TMEM126B. Our data uncovers the functional effect and the molecular mechanism of the pathogenic variants c.82-2 A > G and c.290dupT, which expands the gene mutation spectrum of LLS and clinical spectrum caused by TMEM126B mutations, and thus help to clinical diagnosis of TMEM126B mutation-related mitochondrial diseases.

Neo-adjuvant chemotherapy plus immunotherapy in resectable N1/N2 NSCLC.

BACKGROUND: Locally advanced non-small cell lung cancer (NSCLC) with N1/N2 lymph node metastasis is challenging with poor survival. Neo-adjuvant chemo-immunotherapy has gained benefits in a proportion of these patients. However no specific biomarker has been proved to predict the effect before therapy. In addition, the relationship of nodal status and survival after neo-adjuvant chemo-immunotherapy is still not well stated. METHODS: A total of 75 resectable NSCLC patients with N1/N2 stage who received neo-adjuvant chemo-immunotherapy plus surgery were retrospectively studied. The clinical characteristics, surgical information and safety parameters were collected. The correlations of major pathological response (MPR) and pathological complete response (pCR) with clinical data were analyzed. The progression free disease(PFS) and overall survival(OS) were evaluated with pathological response and nodal status. RESULTS: Of the 75 patients, 69 (92%) patients experienced treatment related adverse effects, while grade 3-4 adverse effects occurred in 8 (10%) patients. All the patients received surgical R0 resection with a MPR rate of 60% and a pCR rate of 36%. 67% of N1 patients and 77% of N2 patients had nodal clearance after neo-adjuvant treatment. A significant difference was observed between pathological response with age, histology and multiple lymph node metastasis. The PFS was better in the MPR cohort. The PFS was 90.1% and 83.6% at the nodal clearance group at the time of 12 and 18 months, compared with 70.1% and 63.7% at the nodal residual group. CONCLUSIONS: The neo-adjuvant chemo-immunotherapy for locally advanced NSCLC with nodal positive was safe and feasible. The patients with elder age and squamous-cell carcinoma (SCC) were more likely to have better pathological response, while multiple nodal metastasis was a negative predictor. The clearance of lymph node resulted in significantly longer PFS and OS.

ACP-Dnnel: anti-coronavirus peptides' prediction based on deep neural network ensemble learning.

The ongoing COVID-19 pandemic has caused dramatic loss of human life. There is an urgent need for safe and efficient anti-coronavirus infection drugs. Anti-coronavirus peptides (ACovPs) can inhibit coronavirus infection. With high-efficiency, low-toxicity, and broad-spectrum inhibitory effects on coronaviruses, they are promising candidates to be developed into a new type of anti-coronavirus drug. Experiment is the traditional way of ACovPs' identification, which is less efficient and more expensive. With the accumulation of experimental data on ACovPs, computational prediction provides a cheaper and faster way to find anti-coronavirus peptides' candidates. In this study, we ensemble several state-of-the-art machine learning methodologies to build nine classification models for the prediction of ACovPs. These models were pre-trained using deep neural networks, and the performance of our ensemble model, ACP-Dnnel, was evaluated across three datasets and independent dataset. We followed Chou's 5-step rules. (1) we constructed the benchmark datasets data1, data2, and data3 for training and testing, and introduced the independent validation dataset ACVP-M; (2) we analyzed the peptides sequence composition feature of the benchmark dataset; (3) we constructed the ACP-Dnnel model with deep convolutional neural network (DCNN) merged the bi-directional long short-term memory (BiLSTM) as the base model for pre-training to extract the features embedded in the benchmark dataset, and then, nine classification algorithms were introduced to ensemble together for classification prediction and voting together; (4) tenfold cross-validation was introduced during the training process, and the final model performance was evaluated; (5) finally, we constructed a user-friendly web server accessible to the public at http://150.158.148.228:5000/ . The highest accuracy (ACC) of ACP-Dnnel reaches 97%, and the Matthew's correlation coefficient (MCC) value exceeds 0.9. On three different datasets, its average accuracy is 96.0%. After the latest independent dataset validation, ACP-Dnnel improved at MCC, SP, and ACC values 6.2%, 7.5% and 6.3% greater, respectively. It is suggested that ACP-Dnnel can be helpful for the laboratory identification of ACovPs, speeding up the anti-coronavirus peptide drug discovery and development. We constructed the web server of anti-coronavirus peptides' prediction and it is available at http://150.158.148.228:5000/ .

PepQSAR: a comprehensive data source and information platform for peptide quantitative structure-activity relationships.

Peptide quantitative structure-activity relationships (pQSARs) have been widely applied to the statistical modeling and empirical prediction of peptide activity, property and feature. In the procedure, the peptide structure is characterized at sequence level using amino acid descriptors (AADs) and then correlated with observations by machine learning methods (MLMs), consequently resulting in a variety of quantitative regression models used to explain the structural factors that govern peptide activities, to generalize peptide properties of unknown from known samples, and to design new peptides with desired features. In this study, we developed a comprehensive platform, termed PepQSAR database, which is a systematic collection and decomposition of various data sources and abundant information regarding the pQSARs, including AADs, MLMs, data sets, peptide sequences, measured activities, model statistics, and literatures. The database also provides a comparison function for the various previously built pQSAR models reported by different groups via distinct approaches. The structured and searchable PepQSAR database is expected to provide a useful resource and powerful tool for the computational peptidology community, which is freely available at http://i.uestc.edu.cn/PQsarDB .

Influence of air mapping errors on the dosimetric accuracy of prostate CBCT-guided online adaptive radiation therapy.

PURPOSE: CBCT-guided online adaptive radiotherapy (oART) plans presently utilize daily synthetic CTs (sCT) that are automatically generated using deformable registration algorithms. These algorithms may have poor performance at reproducing variable volumes of gas present during treatment. Therefore, we have analyzed the air mapping error between the daily CBCTs and the corresponding sCT and explored its dosimetric effect on oART plan calculation. METHODS: Abdominopelvic air volume was contoured on both the daily CBCT images and the corresponding synthetic images for 207 online adaptive pelvic treatments. Air mapping errors were tracked over all fractions. For two case studies representing worst case scenarios, dosimetric effects of air mapping errors were corrected in the sCT images using the daily CBCT air contours, then recalculating dose. Dose volume histogram statistics and 3D gamma passing rates were used to compare the original and air-corrected sCT-based dose calculations. RESULTS: All analyzed patients showed observable air pocket contour differences between the sCT and the CBCT images. The largest air volume difference observed in daily CBCT images for a given patient was 276.3 cc, a difference of more than 386% compared to the sCT. For the two case studies, the largest observed change in DVH metrics was a 2.6% reduction in minimum PTV dose, with all other metrics varying by less than 1.5%. 3D gamma passing rates using 1%/1 mm criteria were above 90% when comparing the uncorrected and corrected dose distributions. CONCLUSION: Current CBCT-based oART workflow can lead to inaccuracies in the mapping of abdominopelvic air pockets from daily CBCT to the sCT images used for the optimization and calculation of the adaptive plan. Despite the large observed mapping errors, the dosimetric effects of such differences on the accuracy of the adapted plan dose calculation are unlikely to cause differences greater than 3% for prostate treatments.

Directly targeting ASC by lonidamine alleviates inflammasome-driven diseases.

BACKGROUND: Dysregulated activation of the inflammasome is involved in various human diseases including acute cerebral ischemia, multiple sclerosis and sepsis. Though many inflammasome inhibitors targeting NOD-like receptor protein 3 (NLRP3) have been designed and developed, none of the inhibitors are clinically available. Growing evidence suggests that targeting apoptosis-associated speck-like protein containing a CARD (ASC), the oligomerization of which is the key event for the assembly of inflammasome, may be another promising therapeutic strategy. Lonidamine (LND), a small-molecule inhibitor of glycolysis used as an antineoplastic drug, has been evidenced to have anti-inflammation effects. However, its anti-inflammatory mechanism is still largely unknown. METHODS: Middle cerebral artery occlusion (MCAO), experimental autoimmune encephalomyelitis (EAE) and LPS-induced sepsis mice models were constructed to investigate the therapeutic and anti-inflammasome effects of LND. The inhibition of inflammasome activation and ASC oligomerization by LND was evaluated using western blot (WB), immunofluorescence (IF), quantitative polymerase chain reaction (qPCR) and enzyme-linked immunosorbent assay (ELISA) in murine bone marrow-derived macrophages (BMDMs). Direct binding of LND with ASC was assessed using molecular mock docking, surface plasmon resonance (SPR), and drug affinity responsive target stability (DARTS). RESULTS: Here, we find that LND strongly attenuates the inflammatory injury in experimental models of inflammasome-associated diseases including autoimmune disease-multiple sclerosis (MS), ischemic stroke and sepsis. Moreover, LND blocks diverse types of inflammasome activation independent of its known targets including hexokinase 2 (HK2). We further reveal that LND directly binds to the inflammasome ligand ASC and inhibits its oligomerization. CONCLUSIONS: Taken together, our results identify LND as a broad-spectrum inflammasome inhibitor by directly targeting ASC, providing a novel candidate drug for the treatment of inflammasome-driven diseases in clinic.

GGPP depletion initiates metaflammation through disequilibrating CYB5R3-dependent eicosanoid metabolism.

Metaflammation is a primary inflammatory complication of metabolic disorders characterized by altered production of many inflammatory cytokines, adipokines, and lipid mediators. Whereas multiple inflammation networks have been identified, the mechanisms by which metaflammation is initiated have long been controversial. As the mevalonate pathway (MVA) produces abundant bioactive isoprenoids and abnormal MVA has a phenotypic association with inflammation/immunity, we speculate that isoprenoids from the MVA may provide a causal link between metaflammation and metabolic disorders. Using a line with the MVA isoprenoid producer geranylgeranyl diphosphate synthase (GGPPS) deleted, we find that geranylgeranyl pyrophosphate (GGPP) depletion causes an apparent metaflammation as evidenced by abnormal accumulation of fatty acids, eicosanoid intermediates, and proinflammatory cytokines. We also find that GGPP prenylate cytochrome b5 reductase 3 (CYB5R3) and the prenylated CYB5R3 then translocate from the mitochondrial to the endoplasmic reticulum (ER) pool. As CYB5R3 is a critical NADH-dependent reductase necessary for eicosanoid metabolism in ER, we thus suggest that GGPP-mediated CYB5R3 prenylation is necessary for metabolism. In addition, we observe that pharmacological inhibition of the MVA pathway by simvastatin is sufficient to inhibit CYB5R3 translocation and induces smooth muscle death. Therefore, we conclude that the dysregulation of MVA intermediates is an essential mechanism for metaflammation initiation, in which the imbalanced production of eicosanoid intermediates in the ER serve as an important pathogenic factor. Moreover, the interplay of MVA and eicosanoid metabolism as we reported here illustrates a model for the coordinating regulation among metabolite pathways.

The intragenic microRNA miR199A1 in the dynamin 2 gene contributes to the pathology of X-linked centronuclear myopathy.

Mutations in the myotubularin 1 (MTM1) gene can cause the fatal disease X-linked centronuclear myopathy (XLCNM), but the underlying mechanism is incompletely understood. In this report, using an Mtm1-/y disease model, we found that expression of the intragenic microRNA miR-199a-1 is up-regulated along with that of its host gene, dynamin 2 (Dnm2), in XLCNM skeletal muscle. To assess the role of miR-199a-1 in XLCNM, we crossed miR-199a-1-/- with Mtm1-/y mice and found that the resultant miR-199a-1-Mtm1 double-knockout mice display markers of improved health, as evidenced by lifespans prolonged by 30% and improved muscle strength and histology. Mechanistic analyses showed that miR-199a-1 directly targets nonmuscle myosin IIA (NM IIA) expression and, hence, inhibits muscle postnatal development as well as muscle maturation. Further analysis revealed that increased expression and phosphorylation of signal transducer and activator of transcription 3 (STAT3) up-regulates Dnm2/miR-199a-1 expression in XLCNM muscle. Our results suggest that miR-199a-1 has a critical role in XLCNM pathology and imply that this microRNA could be targeted in therapies to manage XLCNM.

Furanaspermeroterpenes A and B, two unusual meroterpenoids with a unique 6/6/6/5/5 pentacyclic skeleton from the Marine-derived fungus Aspergillus terreus GZU-31-1.

Furanaspermeroterpenes A (1) and B (2), with a unique 6/6/6/5/5 pentacyclic skeleton, and five new congeners aspermeroterpenes D-H (3-7) were co-isolated from the marine-derived fungus Aspergillus terreus GZU-31-1. Among them, compounds 1 and 2 with rare five-membered D/E coupling rings were the first example of DMOA-derived meroterpenoids. Moreover, compound 3 was the first reported 6/6/6/6/5 pentacyclic meroterpenoid featuring an unusual cis-fused A/B ring. In the bioassays, all of the isolates were evaluated on the inhibitory activities against lipopolysaccharide-induced nitric oxide production in RAW 264.7 cells, and compounds 3-7 exhibited significant anti-inflammatory activity with IC50 values ranging from 6.74 to 29.59 µM than positive control (Indomethacin, IC50 30.98 µM).

Golgi-resident TRIO regulates membrane trafficking during neurite outgrowth.

Neurite outgrowth requires coordinated cytoskeletal rearrangements in the growth cone and directional membrane delivery from the neuronal soma. As an essential Rho guanine nucleotide exchange factor (GEF), TRIO is necessary for cytoskeletal dynamics during neurite outgrowth, but its participation in the membrane delivery is unclear. Using co-localization studies, live-cell imaging, and fluorescence recovery after photobleaching analysis, along with neurite outgrowth assay and various biochemical approaches, we here report that in mouse cerebellar granule neurons, TRIO protein pools at the Golgi and regulates membrane trafficking by controlling the directional maintenance of both RAB8 (member RAS oncogene family 8)- and RAB10-positive membrane vesicles. We found that the spectrin repeats in Golgi-resident TRIO confer RAB8 and RAB10 activation by interacting with and activating the RAB GEF RABIN8. Constitutively active RAB8 or RAB10 could partially restore the neurite outgrowth of TRIO-deficient cerebellar granule neurons, suggesting that TRIO-regulated membrane trafficking has an important functional role in neurite outgrowth. Our results also suggest cross-talk between Rho GEF and Rab GEF in controlling both cytoskeletal dynamics and membrane trafficking during neuronal development. They further highlight how protein pools localized to specific organelles regulate crucial cellular activities and functions. In conclusion, our findings indicate that TRIO regulates membrane trafficking during neurite outgrowth in coordination with its GEF-dependent function in controlling cytoskeletal dynamics via Rho GTPases.

Comparative analyses of the Sox9a-Amh-Cyp19a1a regulatory Cascade in Autotetraploid fish and its diploid parent.

BACKGROUND: Autotetraploid Carassius auratus (4nRCC, 4n = 200, RRRR) was derived from the whole genome duplication of diploid red crucian carp (Carassius auratus red var.) (2nRCC, 2n = 100, RR). To investigate the genetic effects of tetraploidization, we analyzed DNA variation, epigenetic modification and gene expression changes in the Sox9a-Amh-Cyp19a1a regulatory cascade between 4nRCC and 2nRCC. RESULTS: We found that the Sox9a gene contained two variants in 2nRCC and four variants in 4nRCC. Compared with that in 2nRCC, DNA methylation in the promoter regions of the Amh and Cyp19a1a genes in 4nRCC was altered by single nucleotide polymorphism (SNP) mutations, which resulted in the insertions and deletions of CpG sites, and the methylation levels of the Sox9a, Amh and Cyp19a1a genes increased after tetraploidization. The gene expression level of the Sox9a-Amh-Cyp19a1a regulatory cascade was downregulated in 4nRCC compared with that in 2nRCC. CONCLUSION: The above results demonstrate that tetraploidization leads to significant changes in the genome, epigenetic modification and gene expression in the Sox9a-Amh-Cyp19a1a regulatory cascade; these findings increase the extant knowledge regarding the effects of polyploidization.

Ruthenium Nanoparticles Anchored on Graphene Hollow Nanospheres Superior to Platinum for the Hydrogen Evolution Reaction in Alkaline Media.

The design and construction of highly efficient and stable Pt-free catalysts for the electrocatalytic hydrogen evolution reaction (HER) in alkaline media is extremely desirable. Herein, a novel hybrid of ruthenium (Ru) nanoparticles anchored on graphene hollow nanospheres (GHSs) is synthesized by a template-assisted strategy. The combination of ultrafine Ru nanoparticles and hollow spherical support endows the resultant Ru/GHSs an extraordinary catalytic performance with a low overpotential of 24.4 mV at a current density of 10 mA cm-2, a small Tafel slope of 34.8 mV dec-1, as well as long-term stability in 1.0 M KOH solution, which is, to our knowledge, superior to commercial 20% Pt-C catalyst and most of the state-of-the-art HER electrocatalysts reported. Remarkably, this work provides a new route for the development of other metal-based HER electrocatalysts for energy-related applications.

Anaerobic Dehalogenation by Reduced Aqueous Biochars.

Dehalogenation is one of the most important reactions for eliminating trace organic pollutants in natural and engineering systems. This study investigated the dehalogenation of a model organohalogen compound, triclosan (TCS), by aqueous biochars (a-BCs) (<450 nm). We found that TCS can be anaerobically degraded by reduced a-BCs with a pseudo first-order degradation rate constant of 0.0011-0.011 h-1. The 288 h degradation fraction of TCS correlated significantly with the amount of a-BC-bound electrons (0.055 ± 0.00024 to 0.11 ± 0.0016 mol e-/mol C) available for donation after 24 h of pre-reduction by Shewanella putrefaciens CN32. Within the reduction period, the recovery of chlorine based on residual TCS and generated Cl- ranged from 73.6 to 85.2%, implying that a major fraction of TCS was fully dechlorinated, together with mass spectroscopic analysis of possible degradation byproducts. Least-squares numerical fitting, accounting for the reactions of hydroquinones/semiquinones in a-BCs with TCS and byproducts, can simulate the reaction kinetics well (R2 > 0.76) and suggest the first-step dechlorination as the rate-limiting step among the possible pathways. These results showcased that the reduced a-BCs can reductively degrade organohalogens with potential applications for wastewater treatment and groundwater remediation. While TCS was used as a model compound in this study, a-BC-based degradation can be likely applied to a range of redox-sensitive trace organic compounds.

Automated segmentation and diagnosis of pneumothorax on chest X-rays with fully convolutional multi-scale ScSE-DenseNet: a retrospective study.

BACKGROUND: Pneumothorax (PTX) may cause a life-threatening medical emergency with cardio-respiratory collapse that requires immediate intervention and rapid treatment. The screening and diagnosis of pneumothorax usually rely on chest radiographs. However, the pneumothoraces in chest X-rays may be very subtle with highly variable in shape and overlapped with the ribs or clavicles, which are often difficult to identify. Our objective was to create a large chest X-ray dataset for pneumothorax with pixel-level annotation and to train an automatic segmentation and diagnosis framework to assist radiologists to identify pneumothorax accurately and timely. METHODS: In this study, an end-to-end deep learning framework is proposed for the segmentation and diagnosis of pneumothorax on chest X-rays, which incorporates a fully convolutional DenseNet (FC-DenseNet) with multi-scale module and spatial and channel squeezes and excitation (scSE) modules. To further improve the precision of boundary segmentation, we propose a spatial weighted cross-entropy loss function to penalize the target, background and contour pixels with different weights. RESULTS: This retrospective study are conducted on a total of eligible 11,051 front-view chest X-ray images (5566 cases of PTX and 5485 cases of Non-PTX). The experimental results show that the proposed algorithm outperforms the five state-of-the-art segmentation algorithms in terms of mean pixel-wise accuracy (MPA) with [Formula: see text] and dice similarity coefficient (DSC) with [Formula: see text], and achieves competitive performance on diagnostic accuracy with 93.45% and [Formula: see text]-score with 92.97%. CONCLUSION: This framework provides substantial improvements for the automatic segmentation and diagnosis of pneumothorax and is expected to become a clinical application tool to help radiologists to identify pneumothorax on chest X-rays.

Size-resolved surface deposition and coagulation of indoor particles.

This study aimed to investigate the influence of surface deposition and coagulation on indoor particles larger than 0.25 µm by conducting tests in a room-sized enclosed chamber under different air temperatures. The particles, processed dust intercepted by indoor air conditioners, were generated using an aerosol generator. The deposition rate and coagulation coefficients were used to estimate the efficiency of indoor particle surface deposition and coagulation in this study. The results show that the deposition rates increase as the air temperature rises, and high temperatures can also increase the coagulation coefficient. In addition, test results show that the enhancement of indoor air mixing intensity can increase both the deposition rates and the coagulation efficiencies. The contribution of coagulation to the total decay of indoor particle concentrations decreases over time, and the contribution is higher for particles in the range of 0.25-0.5 µm than those in the range of 0.5-1.0 µm.

SRSF6-regulated alternative splicing that promotes tumour progression offers a therapy target for colorectal cancer.

OBJECTIVE: To investigate the molecular function of splicing factor SRSF6 in colorectal cancer (CRC) progression and discover candidate chemicals for cancer therapy through targeting SRSF6. DESIGN: We performed comprehensive analysis for the expression of SRSF6 in 311 CRC samples, The Cancer Genome Atlas and Gene Expression Omnibus (GEO) database. Functional analysis of SRSF6 in CRC was performed in vitro and in vivo. SRSF6-regulated alternative splicing (AS) and its binding motif were identified by next-generation RNA-sequencing and RNA immunoprecipitation sequencing (RIP-seq), which was validated by gel shift and minigene reporter assay. ZO-1 exon23 AS was investigated to mediate the function of SRSF6 in vitro and in vivo. Based on the analysis of domain-specific role, SRSF6-targeted inhibitor was discovered de novoby virtual screening in 4855 FDA-approved drugs and its antitumour effects were evaluated in vitroand in vivo. RESULTS: SRSF6 was frequently upregulated in CRC samples and associated with poor prognosis, which promoted proliferation and metastasis in vitro and in vivo. We identified SRSF6-regulated AS targets and discovered the SRSF6 binding motif. Particularly, SRSF6 regulates ZO-1 aberrant splicing to function as an oncogene by binding directly to its motif in the exon23. Based on the result that SRSF6 RRM2 domain plays key roles in regulating AS and biological function, indacaterol, a ß2-adrenergic receptor agonist approved for chronic obstructive pulmonary disease treatment, is identified as the inhibitor of SRSF6 to suppress CRC tumourigenicity. CONCLUSIONS: SRSF6 functions the important roles in mediating CRC progression through regulating AS, and indacaterol is repositioned as an antitumour drug through targeting SRSF6. ACCESSION NUMBERS: The accession numbers for sequencing data are SRP111763 and SRP111797.