How Does the Number of Brain Metastases Correlate With Normal Brain Exposure in Single-Isocenter Multitarget Multifraction Stereotactic Radiosurgery.

Purpose: To investigate the relationship between normal brain exposure in LINAC-based single-isocenter multitarget multifraction stereotactic radiosurgery or stereotactic radiation therapy (SRT) and the number or volume of treated brain metastases, especially for high numbers of metastases. Methods and Materials: A cohort of 44 SRT patients with 709 brain metastases was studied. Renormalizing to a uniform prescription of 27 Gy in 3 fractions, normal brain dose volume indices, including V23 Gy (volume receiving >23 Gy), V18 Gy (volume receiving >18 Gy), and mean dose, were evaluated on these plans against the number and the total volume of targets for each plan. To compare with exposures from whole-brain radiation therapy (WBRT), the SRT dose distributions were converted to equivalent dose in 3 Gy fractions (EQD3) using an alpha-beta ratio of 2 Gy. Results: With increasing number of targets and increasing total target volume, normal brain exposures to dose ≥18 Gy increases, and so does the mean normal brain dose. The factors of the number of targets and the total target volume are both significant, although the number of targets has a larger effect on the mean normal brain dose and the total target volume has a larger effect on V23 Gy and V18 Gy. The EQD3 mean normal brain dose with SRT planning is lower than conventional WBRT. On the other hand, SRT results in higher hot spot (ie, maximum dose outside of tumor) EQD3 dose than WBRT. Conclusions: Based on clinical SRT plans, our study provides information on correlations between normal brain exposure and the number and total volume of targets. As SRT becomes more greatly used for patients with increasingly extensive brain metastases, more clinical data on outcomes and toxicities is necessary to better define the normal brain dose constraints for high-exposure cases and to optimize the SRT management for those patients.

Machine Learning-Accelerated First-Principles Study of Atomic Configuration and Ionic Diffusion in Li10GeP2S12 Solid Electrolyte.

The solid electrolyte Li10GeP2S12 (LGPS) plays a crucial role in the development of all-solid-state batteries and has been widely studied both experimentally and theoretically. The properties of solid electrolytes, such as thermodynamic stability, conductivity, band gap, and more, are closely related to their ground-state structures. However, the presence of site-disordered co-occupancy of Ge/P and defective fractional occupancy of lithium ions results in an exceptionally large number of possible atomic configurations (structures). Currently, the electrostatic energy criterion is widely used to screen favorable candidates and reduce computational costs in first-principles calculations. In this study, we employ the machine learning- and active-learning-based LAsou method, in combination with first-principles calculations, to efficiently predict the most stable configuration of LGPS as reported in the literature. Then, we investigate the diffusion properties of Li ions within the temperature range of 500-900 K using ab initio molecular dynamics. The results demonstrate that the atomic configurations with different skeletons and Li ion distributions significantly affect the Li ions' diffusion. Moreover, the results also suggest that the LAsou method is valuable for refining experimental crystal structures, accelerating theoretical calculations, and facilitating the design of new solid electrolyte materials in the future.

DOTAD: A Database of Therapeutic Antibody Developability.

The development of therapeutic antibodies is an important aspect of new drug discovery pipelines. The assessment of an antibody's developability-its suitability for large-scale production and therapeutic use-is a particularly important step in this process. Given that experimental assays to assess antibody developability in large scale are expensive and time-consuming, computational methods have been a more efficient alternative. However, the antibody research community faces significant challenges due to the scarcity of readily accessible data on antibody developability, which is essential for training and validating computational models. To address this gap, DOTAD (Database Of Therapeutic Antibody Developability) has been built as the first database dedicated exclusively to the curation of therapeutic antibody developability information. DOTAD aggregates all available therapeutic antibody sequence data along with various developability metrics from the scientific literature, offering researchers a robust platform for data storage, retrieval, exploration, and downloading. In addition to serving as a comprehensive repository, DOTAD enhances its utility by integrating a web-based interface that features state-of-the-art tools for the assessment of antibody developability. This ensures that users not only have access to critical data but also have the convenience of analyzing and interpreting this information. The DOTAD database represents a valuable resource for the scientific community, facilitating the advancement of therapeutic antibody research. It is freely accessible at http://i.uestc.edu.cn/DOTAD/ , providing an open data platform that supports the continuous growth and evolution of computational methods in the field of antibody development.

The predictive value of computed tomography value on high-resolution images in differentiating invasive from indolent lung adenocarcinoma.

Background: Invasive adenocarcinoma (IA) has a worse prognosis and different clinical management strategies compared to indolent lung adenocarcinoma including adenocarcinoma in situ (AIS) and minimally IA (MIA). The purpose of this study was to evaluate the predictive value of computed tomography (CT) value in differentiating invasive from indolent lung adenocarcinoma. Methods: The pathological diagnoses and imaging data of confirmed lung adenocarcinomas manifested as lung nodules with homogeneous internal density which were surgically resected between August 2021 and July 2022 were retrospectively analyzed. Differences in CT values between invasive and indolent lung adenocarcinomas were compared in the primary cohort (n=766), and receiver operating characteristic (ROC) curve analysis was used to determine the optimal cut-off value. The predictive performance of the cut-off value was evaluated in the validation cohort (n=341). Results: A total of 1,107 lung nodules from 1,014 patients were included in the total cohort. The CT values had a significant difference between invasive and indolent lung adenocarcinomas (P<0.001). Using the primary cohort, we determined the optimal cut-off value of -415 Hounsfield units (HU) of the CT value based on ROC curve, which showed good discrimination between IA and AIS/MIA in both the primary and validation cohorts (sensitivity, 85.98% and 87.42%, specificity, 87.67% and 84.74%, respectively). Conclusions: The CT value of >-415 HU could be an effective predictor of invasive lung adenocarcinoma, thereby providing an appropriate clinical decision guide.

Dosimetric evaluation of LINAC-based single-isocenter multi-target multi-fraction stereotactic radiosurgery with more than 20 targets: comparing MME, HyperArc, and RapidArc.

BACKGROUND: To compare the dosimetric quality of three widely used techniques for LINAC-based single-isocenter multi-target multi-fraction stereotactic radiosurgery (fSRS) with more than 20 targets: dynamic conformal arc (DCA) in BrainLAB Multiple Metastases Elements (MME) module and volumetric modulated arc therapy (VMAT) using RapidArc (RA) and HyperArc (HA) in Varian Eclipse. METHODS: Ten patients who received single-isocenter fSRS with 20-37 targets were retrospectively replanned using MME, RA, and HA. Various dosimetric parameters, such as conformity index (CI), Paddick CI, gradient index (GI), normal brain dose exposures, maximum organ-at-risk (OAR) doses, and beam-on times were extracted and compared among the three techniques. Wilcoxon signed-rank test was used for statistical analysis. RESULTS: All plans achieved the prescribed dose coverage goal of at least 95% of the planning target volume (PTV). HA plans showed superior conformity compared to RA and MME plans. MME plans showed superior GI compared to RA and HA plans. RA plans resulted in significantly higher low and intermediate dose exposure to normal brain compared to HA and MME plans, especially for lower doses of ≥ 8Gy and ≥ 5Gy. No significant differences were observed in the maximum dose to OARs among the three techniques. The beam-on time of MME plans was about two times longer than RA and HA plans. CONCLUSIONS: HA plans achieved the best conformity, while MME plans achieved the best dose fall-off for LINAC-based single-isocenter multi-target multi-fraction SRS with more than 20 targets. The choice of the optimal technique should consider the trade-offs between dosimetric quality, beam-on time, and planning effort.

Synthetic steroid of 5α-Androst-3ß,5α,6ß-Triol alleviates acute lung injury via inhibiting inflammation and oxidative stress.

Acute lung injury (ALI) is a severe and potentially fatal respiratory condition with limited treatment options. The pathological evolution of ALI is driven by persistent inflammation, destruction of the pulmonary vascular barrier and oxidative stress. Evidence from prior investigations has identified 5α-androst-3ß,5α,6ß-Triol (TRIOL), a synthetic analogue of the naturally occurring neuroprotective compound cholestane-3ß,5α,6ß-triol, possesses notable anti-inflammatory and antioxidative properties. However, the precise effects of TRIOL on alleviating lung injury along with the mechanisms, have remained largely unexplored. Here, TRIOL exhibited pronounced inhibitory actions on lipopolysaccharide (LPS)-induced inflammation and oxidative stress damage in both lung epithelial and endothelial cells. This protective effect is achieved by its ability to mitigate oxidative stress and restrain the inflammatory cascade orchestrated by nuclear factor-kappa B (NF-κB), thereby preserving the integrity of the pulmonary epithelial barrier. We further validated that TRIOL can attenuate LPS-induced lung injury in rats and mice by reducing inflammatory cell infiltration and improving pulmonary edema. Furthermore, TRIOL decreased the pro-inflammatory factors and increased of anti-inflammatory factors induced by LPS. In conclusion, our study presents TRIOL as a promising novel candidate for the treatment of ALI.

In Silico Immunogenicity Assessment of Therapeutic Peptides.

The application of therapeutic peptides in clinical practice has significantly progressed in the past decades. However, immunogenicity remains an inevitable and crucial issue in the development of therapeutic peptides. The prediction of antigenic peptides presented by MHC class II is a critical approach to evaluating the immunogenicity of therapeutic peptides. With the continuous upgrade of algorithms and databases in recent years, the prediction accuracy has been significantly improved. This has made in silico evaluation an important component of immunogenicity assessment in therapeutic peptide development. In this review, we summarize the development of peptide-MHC-II binding prediction methods for antigenic peptides presented by MHC class II molecules and provide a systematic explanation of the most advanced ones, aiming to deepen our understanding of this field that requires particular attention.

Establishing and validation of the VBV score for assessing Lung ground-glass nodules based on high-resolution computed tomography.

BACKGROUND: The widespread utilization of chest High-resolution Computed Tomography (HRCT) has prompted detection of pulmonary ground-glass nodules (GGNs) in otherwise asymptomatic individuals. We aimed to establish a simple clinical risk score model for assessing GGNs based on HRCT. METHODS: We retrospectively analyzed 574 GGNs in 574 patients undergoing HOOK-WIRE puncture and pulmonary nodule surgery from January 2014 to November 2018. Clinical characteristics and imaging features of the GGNs were assessed. We analyzed the differences between malignant and benign nodules using binary logistic regression analysis and constructed a simple risk score model, the VBV Score, for predicting the malignancy status of GGNs. Then, we validated this model via other 1200 GGNs in 1041 patients collected from three independent clinical centers in 2022. RESULTS: For the exploratory phase of this study, out of the 574 GGNs, 481 were malignant and 93 were benign. Vacuole sign, air bronchogram, and intra-nodular vessel sign were important indicators of malignancy in GGNs. Then, we derived a VBV Score = vacuole sign + air bronchogram + intra-nodular vessel sign, to predict the malignancy of GGNs, with a sensitivity, specificity, and accuracy of 95.6%, 80.6%, and 93.2%, respectively. We also validated it on other 1200 GGNs, with a sensitivity, specificity, and accuracy of 96.0%, 82.6%, and 95.0%, respectively. CONCLUSIONS: Vacuole sign, air bronchogram, and intra-nodular vessel sign were important indicators of malignancy in GGNs. VBV Score showed good sensitivity, specificity, and accuracy for differentiating benign and malignant pulmonary GGNs.

SAGESDA: Multi-GraphSAGE networks for predicting SnoRNA-disease associations.

Over the years, extensive research has highlighted the functional roles of small nucleolar RNAs in various biological processes associated with the development of complex human diseases. Therefore, understanding the existing relationships between different snoRNAs and diseases is crucial for advancing disease diagnosis and treatment. However, classical biological experiments for identifying snoRNA-disease associations are expensive and time-consuming. Therefore, there is an urgent need for cost-effective computational techniques that can enhance the efficiency and accuracy of prediction. While several computational models have already been proposed, many suffer from limitations and suboptimal performance. In this study, we introduced a novel Graph Neural Network-based (GNN) classification model, called SAGESDA, which is implemented through the GraphSAGE architecture with attention for the prediction of snoRNA-disease associations. The classifier leverages local neighbouring nodes in a heterogeneous network to generate new node embeddings through message passing. The mini-batch gradient descent technique was applied to divide the graph into smaller sub-graphs, which enhances the model's accuracy, speed and scalability. With these advancements, SAGESDA attained an area under the receiver operating characteristic (ROC) curve (AUC) of 0.92 using the standard dot product classifier, surpassing previous related studies. This notable performance demonstrates that SAGESDA is a promising model for predicting unknown snoRNA-disease associations with high accuracy. The SAGESDA implementation details can be obtained from https://github.com/momanyibiffon/SAGESDA.git.

Novel 6/7/6 ring system diterpenoids and cytochalasins from the fungus Eutypella scoparia GZU-4-19Y and their anti-inflammatory activity.

Two new compounds eutyditerpenoid A (1) and seco-phenochalasin B (5), together with seven known compounds diaporthein A (2), aspergillon A (3), phenochalasin B (4), cytochalasins Z24 and Z25 (6 and 7), scoparasins A and B (8 and 9) were isolated from marine-derived Eutypella scoparia GZU-4-19Y. Among them, eutyditerpenoid A (1) with a rare 6/7/6 ring system possesing an anhydride moiety was the first example in the pimarane-type diterpenoids. Their structures were determined based on spectroscopic methods and the electronic circular dichroism (ECD) calculations. In the bioassays, all of the isolates were evaluated for their inhibitory activity against NO production induced by lipopolysaccharide in RAW 264.7 cells. Compounds 3 and 7 showed potent NO inhibition activity with IC50 values of 2.1 and 17.1 µM respectively, and the former also significantly suppressed the protein expression of iNOS and COX-2 at the concentration of 2.5 µM.

Crohn's disease as the intestinal manifestation of pan-lymphatic dysfunction: An exploratory proposal based on basic and clinical data.

Crohn's disease (CD) is caused by immune, environmental, and genetic factors. It can involve the entire gastrointestinal tract, and although its prevalence is rapidly increasing its etiology remains unclear. Emerging biological and small-molecule drugs have advanced the treatment of CD; however, a considerable proportion of patients are non-responsive to all known drugs. To achieve a breakthrough in this field, innovations that could guide the further development of effective therapies are of utmost urgency. In this review, we first propose the innovative concept of pan-lymphatic dysfunction for the general distribution of lymphatic dysfunction in various diseases, and suggest that CD is the intestinal manifestation of pan-lymphatic dysfunction based on basic and clinical preliminary data. The supporting evidence is fully summarized, including the existence of lymphatic system dysfunction, recognition of the inside-out model, disorders of immune cells, changes in cell plasticity, partial overlap of the underlying mechanisms, and common gut-derived fatty and bile acid metabolism. Another benefit of this novel concept is that it proposes adopting the zebrafish model for studying intestinal diseases, especially CD, as this model is good at presenting and mimicking lymphatic dysfunction. More importantly, the ensuing focus on improving lymphatic function may lead to novel and promising therapeutic strategies for CD.

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.

Development of a Set of Wheat-Rye Derivative Lines from Hexaploid Triticale with Complex Chromosomal Rearrangements to Improve Disease Resistance, Agronomic and Quality Traits of Wheat.

An elite hexaploid triticale Yukuri from Australia was used as a bridge for transferring valuable genes from Secale cereale L. into common wheat for enriching the genetic variability of cultivated wheat. Non-denaturing-fluorescence in situ hybridization (ND-FISH) identified that Yukuri was a secondary triticale with a complete set of rye chromosomes and a 6D(6A) substitution. Seed protein electrophoresis showed that Yukuri had a unique composition of glutenin subunits. A set of Yukuri-derived wheat-rye introgression lines were created from a Yukuri x wheat population, and all lines were identified by ND-FISH with multiple probes and validated by diagnostic molecular marker analysis. A total of 59 wheat-rye introgression lines including modified chromosome structural variations of wheat, and new complex recombinant chromosomes of rye were detected through ND-FISH and Oligo-FISH painting based on oligonucleotide pools derived from wheat-barley genome collinear regions. Wheat lines carrying the 1R chromosome from Yukuri displayed resistance to both stripe rust and powdery mildew, while the lines carrying the 3RL and 7RL chromosome arms showed stripe rust resistance. The chromosome 1R-derived lines were found to exhibit a significant effect on most of the dough-related parameters, and chromosome 5R was clearly associated with increased grain weight. The development of the wheat-rye cytogenetic stocks carrying disease resistances and superior agronomic traits, as well as the molecular markers and FISH probes will promote the introgression of abundant variation from rye into wheat improvement programs.

BNTA attenuates temporomandibular joint osteoarthritis progression by directly targeting ALDH3A1: An in vivo and in vitro study.

BNTA is known to have a therapeutic effect on knee osteoarthritis and inflammatory osteoclastogenesis. However, the protective effect of BNTA regarding temporomandibular mandibular joint osteoarthritis (TMJOA) and its underlying mechanism and physiological target remains unclear. In the present study, BNTA ameliorated cartilage degradation and inflammation responses in monosodium iodoacetate (MIA)-induced TMJOA in vivo. In IL-1ß-induced condylar chondrocytes, BNTA prevents oxidative stress, inflammatory responses and increasing synthesis of cartilage extracellular matrix through activating nuclear factor-E2-related factor 2 (NRF2) signaling. Suppression of NRF2 signaling abolishes the protective effect of BNTA in TMJOA. Notably, BNTA may bind directly to ALDH3A1 and act as a stabilizer, as evidenced by drug affinity responsive target stability assay (DARTS), cellular thermal shift assay (CETSA) and molecular docking results. Further investigation of the underlying molecular and cellular mechanism infers a positive correlation of ALDH3A1 regulating NRF2 signaling. In conclusion, BNTA may attenuate TMJOA progression via the ALDH3A1/NRF2 axis, inferring that BNTA is a therapeutic target for treating temporomandibular mandibular joint osteoarthritis.

Antagonizing adipose tissue-derived exosome miR-103-hepatocyte phosphatase and tensin homolog pathway alleviates autophagy in non-alcoholic steatohepatitis: A trans-cellular crosstalk.

BACKGROUND: Obesity plays a vital role in the occurrence and development of non-alcoholic steatohepatitis (NASH). However, the underlining mechanism is still unclear, where adipose tissue (AT) derived exosomes may actively participate. MicroRNAs (miRNAs) are commonly secreted from exosomes for cell communication. Though the regulation of miR-103 on insulin sensitivity has been reported, the specific role of AT-derived exosomes miR-103 in NASH is still vague and further investigation may provide novel therapeutic choices. AIM: To determine the specific role of AT-derived exosomes miR-103 in developing NASH through various methods. METHODS: The expression levels of miR-103 in the AT-derived exosomes and livers were detected and compared between NASH mice and control. The effect of miR-103 on NASH progression was also explored by antagonizing miR-103, including steatosis and inflammation degree changes. The interaction between miR-103 and the autophagy-related gene phosphatase and tensin homolog (PTEN) was confirmed by dual-luciferase reporter assay. The role of the interaction between miR-103 and PTEN on autophagy was verified in NASH-like cells. Finally, the effects of miR-103 from adipose-derived exosomes on NASH and autophagy were analyzed through animal experiments. RESULTS: The expression of miR-103 was increased in NASH mice, compared to the control, and inhibition of miR-103 could alleviate NASH. The results of the dual-luciferase reporter assay showed miR-103 could interact with PTEN. MiR-103-anta decreased p-AMPKa, p-mammalian target of rapamycin (mTOR), and p62 but increased the protein levels of PTEN and LC3-II/I and the number of autophagosomes in NASH mice. Similar results were also observed in NASH-like cells, and further experiments showed PTEN silencing inhibited the effect of miR-103-anta. AT derived-exosome miR-103 aggravated NASH and increased the expressions of p-AMPKa, p-mTOR, and p62 but decreased the protein levels of PTEN and LC3-II/I and the number of autophagosomes in mice. CONCLUSION: AT derived-exosome increased the levels of miR-103 in the liver, and miR-103 aggravated NASH. Mechanically, miR-103 could interact with PTEN and inhibit autophagy.

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/ .

AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains.

Over 120 FDA-approved antibody-based therapeutics are used to treat a variety of diseases.However, many candidates could fail because of unfavorable physicochemical properties. Light-chain amyloidosis is one form of aggregation that can lead to severe safety risks in clinical development. Therefore, screening candidates with a less amyloidosis risk at the early stage can not only save the time and cost of antibody development but also improve the safety of antibody drugs. In this study, based on the dipeptide composition of 742 amyloidogenic and 712 non-amyloidogenic antibody light chains, a support vector machine-based model, AB-Amy, was trained to predict the light-chain amyloidogenic risk. The AUC of AB-Amy reaches 0.9651. The excellent performance of AB-Amy indicates that it can be a useful tool for the in silico evaluation of the light-chain amyloidogenic risk to ensure the safety of antibody therapeutics under clinical development. A web server is freely available at http://i.uestc.edu.cn/AB-Amy/.

Microstructure-united heterogeneous sodium alginate doped injectable hydrogel for stable hemostasis in dynamic mechanical environments.

Injectable hydrogels that can withstand compressive and tensile forces hold great promise for preventing rebleeding in dynamic mechanical environments after emergency hemostasis of wounds. However, current injectable hydrogels often lack sufficient compressive or tensile performance. Here, a microstructure-united heterogeneous injectable hydrogel (MH) was constructed. The heterogeneous structure endowed MH with a unique "microstructures consecutive transmission" feature, which allowed it to exhibit high compressive and tensile performance simultaneously. In this work, two types of sodium alginate doped hydrogels with different microstructures were physically smashed into microgels, respectively. By mixing the microgels, MH with one micro-pores featured microstructure and another nano-pores featured microstructure can be formed. The obtained MH can withstand both compressive and tensile forces and showed high mechanical performance (compressive modulus: 345.67 ± 10.12 kPa and tensile modulus: 245.19 ± 7.82 kPa). Furtherly, MH was proven to provide stable and sustained hemostasis in the dynamic mechanical environment. Overall, this work provided an effective strategy for constructing injectable hydrogel with high compressive and tensile performance for hemostasis in dynamic mechanical environments.

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.

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.