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Deep carbon cycle is crucial for mantle dynamics and maintaining Earth's habitability. Recycled carbonates are a strong oxidant in mantle carbon-iron redox reactions, leading to the formation of highly oxidized mantle domains and deep carbon storage. Here we report high Fe 3+ /∑Fe values in Cenozoic intraplate basalts from eastern China, which are correlated with geochemical and isotopic compositions that point to a common role of carbonated melt with recycled carbonate signatures. We propose that the source of these highly oxidized basalts has been oxidized by carbonated melts derived from the stagnant subducted slab in the mantle transition zone. Diamonds formed during the carbon-iron redox reaction were separated from the melt due to density differences. This would leave a large amount of carbon (about four times of preindustrial atmospheric carbon budget) stored in the deep mantle and isolated from global carbon cycle. As such, the amounts of subducted slabs stagnated at mantle transition zone can be an important factor regulating the climate.

Anther dehiscence is a crucial event in plant reproduction, tightly regulated and dependent on the lignification of the anther endothecium. In this study, we investigated the rapid lignification process that ensures timely anther dehiscence in Arabidopsis. Our findings reveal that endothecium lignification can be divided into two distinct phases. During Phase I, lignin precursors are synthesized without polymerization, while Phase II involves simultaneous synthesis of lignin precursors and polymerization. The transcription factors MYB26, NST1/2, and ARF17 specifically regulate the pathway responsible for the synthesis and polymerization of lignin monomers in Phase II. MYB26-NST1/2 is the key regulatory pathway responsible for endothecium lignification, while ARF17 facilitates this process by interacting with MYB26. Interestingly, our results demonstrate that the lignification of the endothecium, which occurs within approximately 26 h, is much faster than that of the vascular tissue. These findings provide valuable insights into the regulation mechanism of rapid lignification in the endothecium, which enables timely anther dehiscence and successful pollen release during plant reproduction., (© 2024 Institute of Botany, Chinese Academy of Sciences.)

Trace iron ions (Fe(III)) are commonly found in water and wastewater, where free chlorine is very likely to coexist with Fe(III) affecting the disinfectant's stability and N-DBPs' fate during UV/chlorine disinfection, and yet current understanding of these mechanisms is limited. This study investigates the effects of Fe(III) on the formation and toxicity alteration of halonitromethanes (HNMs), dichloroacetonitrile (DCAN), and dichloroacetamide (DCAcAm) from polyethyleneimine (PEI) during UV/chlorine disinfection. Results reveal that the maxima concentrations of HNMs, DCAN, and DCAcAm during UV/chlorine disinfection with additional Fe(III) were 1.39, 1.38, and 1.29 times higher than those without additional Fe(III), instead of being similar to those of Fe(III) inhibited the formation of HNMs, DCAN and DCAcAm during chlorination disinfection. Meanwhile, higher Fe(III) concentration, acidic pH, and higher chlorine dose were more favorable for forming HNMs, DCAN, and DCAcAm during UV/chlorine disinfection, which were highly dependent on the involvement of HO· and Cl·. Fe(III) in the aquatic environment partially hydrolyzed to the photoactive Fe(III)‑hydroxyl complexes Fe(OH) 2+ and [Fe(H 2 O) 6 ] 3+ , which undergone UV photoactivation and coupling reactions with HOCl to achieve effective Fe(III)/Fe(II) interconversion, a process that facilitated the sustainable production of HO·. Extensive product analysis and comparison verified that the HO· production enhanced by the Fe(III)/Fe(II) internal cycle played a primary role in increasing HNMs, DCAN, and DCAcAm productions during UV/chlorine disinfection. Note that the incorporation of Fe(III) increased the cytotoxicity and genotoxicity of HNMs, DCAN, and DCAcAm formed during UV/chlorine disinfection, and yet Fe(III) did not have a significant effect on the acute toxicity of water samples before, during, and after UV/chlorine disinfection. The new findings broaden the knowledge of Fe(III) affecting HNMs, DCAN, and DCAcAm formation and toxicity alteration during UV/chlorine disinfection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Limited knowledge exists regarding the occurrence, potential sources, and risks of contaminants of emerging concern (CECs) in surface and waste water from chemical contiguous zones. A total of 136 CECs were detected at 32 sampling sites along the Yangtze River, with concentrations ranging from 0.55 to 4.21 × 10 4  ng/L. Hydrocortisonacetate, cortisone, prednisone, enalapril and medroxyprogesterone were detected across all sampling sites. Hierarchical cluster analysis based on 47 core CECs yielded similar results compared with principal components analysis and identified two major clusters: wastewater sites and surface water sites. Distinct patterns of CECs were observed in wastewater from three industrial parks owing to variations in the industrial facilities and products within each park. Nineteen CECs were initially classified as presenting a high or medium risk to aquatic organisms. Further quantitative probabilistic risk assessment revealed that caffeine, trenbolone and norethindrone posed a threat to the most vulnerable aquatic species while high-risk sites mainly occurred downstream of the chemical industrial park. The joint ecological risk of high-risk CECs was evaluated using potentially affected fractions, which ranged from 0.44 % to 47.9 % with concentration addition and 0.33 % to 45.1 % with response addition. This suggests the need to consider the joint ecological risk of the detected compounds in future studies., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Chlorinated hydrocarbons (CHs) pose significant health risks due to their suspected carcinogenicity, necessitating urgent remediation efforts. While the combination of zero-valent iron (Fe 0 ) and microbial action shows promise in mitigating CH contamination, field studies on this approach are scarce. We devised a novel three-layer permeable reactive barrier (PRB) material incorporating Fe 0 and coconut shell biochar, effectively implemented at a typical CH-contaminated site. Field monitoring data revealed conducive conditions for reductive dechlorination of CHs, characterized by low oxygen levels and a relatively neutral pH in the groundwater. The engineered PRB material consistently released organic carbon and iron, fostering the proliferation of CH-dechlorinating bacteria. Over a 250-day operational period, the pilot-scale PRB demonstrated remarkable efficacy in CH removal, achieving removal efficiencies ranging from 21.9% to 99.6% for various CH compounds. Initially, CHs were predominantly eliminated through adsorption and iron-mediated reductive dechlorination. However, microbial reductive dechlorination emerged as the predominant mechanism for sustained and long-term CHs removal. These findings underscore the economic viability and effectiveness of our approach in treating CH-contaminated groundwater, offering promising prospects for broader application in environmental remediation efforts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective To analyze the research progress and hot topics in hypertrophic cardiomyopathy from 2018 to 2022.Methods The publications in the field of hypertrophic cardiomyopathy from January 1,2018 to December 31,2022 were retrieved from Web of Science core collection database and included for a bibliometric analysis.Results A total of 6355 publications were included,with an average citation frequency of 7 times.The year 2021 witnessed the most publications (1406).The analysis with VOSviewer showed that the research on sudden death related to hypertrophic cardiomyopathy,especially the predictive value of late gadolinium-enhanced cardiac MRI in sudden death,was a hot topic.In addition,gene detection and the new drug mavacamten became hot research topics.The United States was the country with the largest number of publications and the highest citation frequency in this field.Chinese scholars produced the second largest number of publications,which,however,included few high-quality research results.Conclusions Risk stratification and prevention of sudden death is still an important and hot research content in the field of hypertrophic cardiomyopathy.Chinese scholars should carry out multi-center cooperation in the future to improve the research results.

Minimally invasive thermal therapy is a successful alternative treatment to surgery in solid tumors with high complete ablation rates, however, tumor recurrence remains a concern. Central memory CD8 + T cells (T CM ) play important roles in protection from chronic infection and cancer. Here we find, by single-cell RNA analysis of human breast cancer samples, that although the memory phenotype of peripheral CD8 + T cells increases slightly after microwave ablation (MWA), the metabolism of peripheral CD8 + T cells remains unfavorable for memory phenotype. In mouse models, glycolysis inhibition by 2-deoxy-D-glucose (2DG) in combination with MWA results in long-term anti-tumor effect via enhancing differentiation of tumor-specific CD44 hi CD62L + CD8 + T CM cells. Enhancement of CD8 + T CM cell differentiation determined by Stat-1, is dependent on the tumor-draining lymph nodes (TDLN) but takes place in peripheral blood, with metabolic remodeling of CD8 + T cells lasting the entire course of the the combination therapy. Importantly, in-vitro glycolysis inhibition in peripheral CD8 + T cells of patients with breast or liver tumors having been treated with MWA thrice leads to their differentiation into CD8 + T CM cells. Our work thus offers a potential strategy to avoid tumor recurrence following MWA therapy and lays down the proof-of-principle for future clinical trials., (© 2024. The Author(s).)

Background: Immune checkpoint blockade has shown low response rates for advanced breast cancer, and combination strategies are needed. Microwave ablation (MWA) may be a trigger of antitumor immunity. This window-of-opportunity trial (ClinicalTrials.gov: NCT04805736) was conducted to determine the safety and feasibility of preoperative camrelizumab (an anti-PD-1 antibody) combined with MWA in the treatment of early-stage breast cancer., Methods: Sixty participants were randomized to preoperatively receive single-dose camrelizumab alone (n = 20), MWA alone (n = 20), or camrelizumab+MWA (n = 20). A random number table was used to allocate interventions. The primary outcome was the safety and feasibility of MWA combined with camrelizumab., Findings: Camrelizumab and MWA were well tolerated alone and in combination without delays in prescheduled surgery. No treatment-related grade III/IV adverse events were observed. Different from in the single-dose camrelizumab or MWA group, participants showed stable counts of blood cells after combination therapy. After combination therapy, peripheral CD8 + T cells showed enhanced cytotoxic and effect-memory functions. Clonal expansional CD8 + T cells showed higher cytotoxic activity and effector memory- and tumor-specific signatures than emergent clones after combination therapy. Enhanced interactions between clonal expansional CD8 + T cells and monocytes were observed, suggesting that monocytes contributed to the enhanced functions of clonal expansional CD8 + T cells. Major histocompatibility complex (MHC) class I-related pathways and interferon signaling pathways were activated in monocytes by combination therapy., Conclusions: Camrelizumab combined with MWA was feasible for early-stage breast cancer. Peripheral CD8 + T cells were activated after combination therapy, dependent on monocytes with activated MHC class I pathways., Funding: This study was supported by the Natural Science Foundation of Jiangsu Province (BK20230017)., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Metastasis is the major cause of cancer-related death, and lymph node is the most common site of metastasis in breast cancer. However, the alterations that happen in tumor-draining lymph nodes (TDLNs) to form a premetastatic microenvironment are largely unknown. Here, we first report the dynamic changes in size and immune status of TDLNs before metastasis in breast cancer. With the progression of tumor, the TDLN is first enlarged and immune-activated at early stage that contains specific antitumor immunity against metastasis. The TDLN is then contracted and immunosuppressed at late stage before finally getting metastasized. Mechanistically, B and follicular helper T (Tfh) cells parallelly expand and contract to determine the size of TDLN. The activation status and specific antitumor immunity of CD8 + T cells in the TDLN are determined by interleukin-21 (IL-21) produced by Tfh cells, thus showing parallel changes. The turn from activated enlargement to suppressed contraction is due to the spontaneous contraction of germinal centers mediated by follicular regulatory T cells. On the basis of the B-Tfh-IL-21-CD8 + T cell axis, we prove that targeting the axis could activate TDLNs to resist metastasis. Together, our findings identify the dynamic alterations and regulatory mechanisms of premetastatic TDLNs of breast cancer and provide new strategies to inhibit lymph node metastasis., Competing Interests: Competing interests: The authors declare that they have no competing interests., (Copyright © 2024 Xinrui Mao et al.)

Significance: Venous leg ulcers (VLUs) are the most common venous disease, mainly presenting as open skin lesions on the legs or feet and are an important concern in clinical care settings. Recent Advances: Comprehensive tactics were employed to search electronic databases PubMed, Embase, guideline databases, and society websites were searched for Clinical Practice Guidelines (CPGs) on VLU care. The basic information, recommendations for the VLUs, methodological quality, and reporting quality of VLU's CPGs were extracted and captured in Excel. The quality of each CPG was independently assessed by four researchers using AGREE II instrument and the RIGHT checklist. Critical Issues: This study included 19 CPGs with a combined 23 recommendations. The assessment of VLUs was summarized based on the recommendations of VLUs in 11 major items; six on VLU's diagnosis and six on therapeutic strategies of VLUs. The identified CPGs were of mixed quality, and the highest score based on the scope and purpose was 82.85 ± 11.66, whereas the lowest mean score based on the editorial independence by AGREE II was 59.93 ± 21.50. Regarding the RIGHT checklist, field one (basic information) had the highest reporting rate (84.33%), whereas field five (review and quality assurance) had the lowest quality of CPGs (41.11%). Future Directions: This evidence map provided new perspectives in the presentation of evidence. In addition, the evidence map collected and evaluated the characteristics of published CPGs. Thus, the evidence map enhances our knowledge and promotes the development of trustworthy CPGs for VLUs.

Background: Multiphase contrast-enhanced computed tomography (CECT) is a commonly used modality in pediatric computed tomography (CT) scans. However, the purposes and focus of each phase, such as CT angiography (CTA), and parenchymal phase, are different. In routine practice, the same scanning parameters are used for all phases, resulting in unnecessary radiation exposure for children. Accurately and rapidly adjusting the scanning parameters for each phase of CECT is challenging in clinical settings. This retrospective cross-sectional study was designed to investigate the feasibility of using both CARE kV and CARE Dose 4D to reduce the radiation dose while maintaining diagnostic quality in multiphase CECT scans of children., Methods: Overall, 57 children (33 males and 24 females) who underwent multiphase abdominal CECT in Xinjiang Hospital of Beijing Children's Hospital with an average age of 6.52±4.30 years (range, 0.1-15 years), were enrolled. The tube voltage was automatically modulated using CARE kV. The tube current was automatically modulated using CARE Dose 4D. Different dose saving optimization indices (DI) were used for the three phases: a DI value of 3 was used for the unenhanced CT phase, a DI value of 12 was used for the CTA phase, and a DI value of 7 was used for the parenchymal phase. The tube voltage and volume CT dose index (CTDI vol ) were recorded for each phase. Two reviewers subjectively evaluated the overall image quality and noise level of the three phases using a 5-point Likert scale (1-2 points: unqualified, 3 points: qualified, 4 points: better, 5 points: best). The CT and noise values of the descending aorta, liver, and back muscle were measured objectively. The voltage distribution and the image quality and CTDI vol in each phase were compared., Results: The most selected tube voltage in the unenhanced CT, CTA, and parenchymal phases was 100 kV (49/57, 85.96%), 70 kV (36/57, 63.16%), and 80 kV (32/57, 56.14%), respectively. The differences between the three phases were statistically significant (P<0.001). The CTDI vol values of the three phases were 3.99±1.99, 2.02±1.71, and 3.18±2.10 mGy, respectively, with a significant difference between the three phases (P<0.001). The CTDI vol decreased linearly as the DI value increased. All images met the diagnostic requirements. The overall quality scores for the three phases were 4.24±0.42, 4.41±0.49, and 4.50±0.45, respectively, with no significant linear relationship with the change in the DI., Conclusions: The combined use of CARE Dose 4D and CARE kV could effectively reduce the radiation dose in children during multiphase abdominal CECT without compromising the diagnostic image quality., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-1181/coif). The authors have no conflicts of interest to declare., (2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Purpose: This study aimed to explore a novel position of mammography named axilla view in axillary lymph node (ALN) evaluation in breast cancer., Patients and Methods: Patients were prospectively enrolled and scheduled for mammography before surgery. Investigated imaging patterns included mediolateral oblique (2D-MLO) and axilla view (2D-axilla) of mammography, and axilla view of digital breast tomosynthesis (3D-axilla). The correlation of ALN numbers between imaging and pathology was analyzed. Diagnostic performance was analyzed via AUC., Results: 75 patients were included. A larger and clearer axillary region was displayed in axilla view. The total number of ALNs detected under 2D/3D-axilla view was significantly higher than that under 2D-MLO view (4.6 vs. 2.5, P < .001; 5.6 vs. 4.6, P = .034). Correlations between number of positive ALNs detected under 2D/3D-axilla view and pathologically confirmed metastatic ALNs were stronger than 2D-MLO view (Pearson correlation coefficients: 0.7084,0.7044 and 0.4744). The proportion of cases with ≥5 positive ALNs detected under 3D-axilla view was significantly higher than that under 2D-MLO (38.2% vs. 14.7%, P = .028). The overweight and obese group showed a higher AUC value than the underweight and lean group in ALN evaluation, although not significantly (2D-MLO: 0.7643 vs. 0.6458, P = .2656; 2D-axilla: 0.8083 vs. 0.6586, P = .1522; 3D-axilla: 0.8045 vs. 0.6615, P = .1874). This difference was more pronounced in axilla view., Conclusion: Axilla view exhibited advantages over conventional MLO view in the extent of axilla displayed by mammography in breast cancer. Further studies with larger sample sizes are needed., Competing Interests: Disclosure The authors have stated that they have no conflicts of interest., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Adipose-derived stem cells (ADSCs) are closely associated with the survival rate of transplanted fat in breast reconstruction after breast cancer surgery. Nevertheless, the intrinsic mechanisms regulating ADSCs adipogenic differentiation remain ambiguous. The aim of our study was to explore the relevant genes and pathways to elucidate the potential mechanisms of adipogenic differentiation in ADSCs., Methods: The Gene Expression Omnibus (GEO) dataset GSE61302 was downloaded and analyzed to identify differentially expressed genes (DEGs). Key genes and signaling pathways were obtained through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional and enrichment analysis. Protein-protein interaction (PPI) network and hub gene analyses were performed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and Cytoscape software. Finally, the transcription levels of hub genes in the adipogenic differentiated group and undifferentiated group of ADSCs were compared via real-time quantitative polymerase chain reaction (RT-qPCR)., Results: In total, 1,091 DEGs were identified through bioinformatics analysis of the adipogenic differentiated group and undifferentiated group. If was then found that the 10 downregulated key genes, CCNB1, NUSAP1, DLGAP5, TTK, CCNB2, KIF23, BUB1B, CDC20, CDCA8 , and KIF11 may play important roles in the adipogenic differentiation of ADSCs. Subsequent in vitro experimental verification also revealed that the messenger RNA (mRNA) expression levels of cyclin B1 in adipogenic differentiated cells and undifferentiated cells were significantly different at the early stage (P<0.05), but there was no significant difference at the late stage (P>0.05)., Conclusions: As a key gene, CCNB1 might be a potential biomarker in the adipogenic differentiation of ADSCs at the early stage., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://gs.amegroups.com/article/view/10.21037/gs-23-493/coif). Z.H.W. reports this research was funded by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. JX10214035). The other authors have no conflicts of interest to declare., (2024 Gland Surgery. All rights reserved.)

Introduction: Locoregional recurrent breast cancers have a poor prognosis. Little is known about the prognostic impact of immune microenvironment, and tertiary lymphoid structures (TLSs) in particular have not been reported. Thus, we aimed to characterize the immune microenvironment in locoregional recurrent breast tumors and to investigate its relationship with prognosis., Methods: We retrospectively included 112 patients with locoregional recurrent breast cancer, and hematoxylin-eosin staining and immunohistochemical staining (CD3, CD4, CD8, CD19, CD38, and CD68) were performed on locoregional recurrent tumor samples. The association of immune cells and TLSs with progression-free survival (PFS) were analyzed by survival analysis., Results: We found more immune cells in the peritumor than stroma. After grouping according to estrogen receptor (ER) status, a low level of peritumoral CD3+ cells in ER+ subgroup (p = 0.015) and a low level of stromal CD68+ cells in ER- subgroup (p = 0.047) were both associated with longer PFS. TLSs were present in 68% of recurrent tumors, and CD68+ cells within TLSs were significantly associated with PFS as an independent prognostic factor (p = 0.035). TLSs and immune cells (CD3, CD38, and CD68) within TLSs were associated with longer PFS in ER- recurrent tumors (p = 0.044, p = 0.012, p = 0.050, p < 0.001, respectively), whereas CD38+ cells within TLSs were associated with shorter PFS in ER+ recurrent tumors (p = 0.037)., Conclusion: Our study proposes potential predictors for the clinical prognosis of patients with locoregional recurrent breast cancer, emphasizing the prognostic value of immune cells within TLSs, especially CD68+ cells., (© 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Context: The safety and efficacy of nutritional management for pressure injuries (PIs) have been the subjects of ongoing interest. Some evidence demonstrated that nutrition is essential for skin and tissue viability, supporting tissue repair for healing the pressure injury., Objective: This investigation aimed to systematically review clinical practice guidelines (CPGs) for the nutritional management of PIs and furnish an evidence map to assess research trends and CPG gaps., Methods: The PubMed, Embase, and guidelines databases, and society websites were searched for CPGs for the nutritional management of PIs. The basic recommendations for the nutritional management of PIs, method quality, and reporting CPGs quality were identified and imported into Excel. Four researchers independently elucidated each CPG's quality via the Appraisal of Guidelines for Research & Evaluation (AGREE) II instrument and the Reporting Items for Practice Guidelines in Healthcare (RIGHT) checklist. All bubble charts were generated using Excel software., Results: This review included 12 CPGs with a combined 23 recommendations. The nutrition screening and assessment were summarized on the basis of the PI recommendations for 6 major items, 12 items on nutrition management, and 3 on PI education. The assessed CPGs had mixed quality, and the highest score  ± standard deviation based on the clarity of presentation was 83.46 ± 7.62, whereas the lowest mean score based on AGREE II applicability was 53.31 ± 16.90. Field 1 (basic information) in the RIGHT checklist had the greatest reporting rate (68.06%), whereas field 5 (review and quality assurance) had the lowest CPGs quality (41.67%)., Conclusion: This investigation furnishes an evidence map and provides new perspectives on the CPGs for the nutritional management of PIs. However, the CPGs included still need improvement, especially in the applicability and editorial independence domains., (© The Author(s) 2023. Published by Oxford University Press on behalf of the International Life Sciences Institute. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The soil ecosystem plays a significant role in the dynamics of microplastics. Qualitative and quantitative analyses of microplastics in six farmland plots from the cities of Nanjing, Suzhou, and Xuzhou were conducted by using laser infrared imaging system (LDIR). The results showed that the abundance of microplastics in farmland soils in Jiangsu Province ranged from 667-9333 items/kg, and the difference between the abundance of microplastics in soils from open-field cultivation and greenhouse farming was not significant. The vertical distribution characteristics showed that the abundance of microplastic decreased significantly with the increase of soil depth. However, there was no significant difference in particle size between top soil (0-5 cm) and deep soil (10-20 cm). Fragments were the most common microplastic form in soil samples (94.6%), with polyvinyl chloride (PVC) being the main polymer type, followed by polyethylene terephthalate (PET). Compared with previous studies conducted in China, the abundance of microplastics in farmland soils in Jiangsu Province was at moderate level but was notably higher than other places within the Yangtze River Delta region. The conclusions drawn from this paper provided important reference data for future assessment of microplastic pollution in agricultural fields. Furthermore, they establish a fundamental groundwork for understanding the migration patterns of MPs in soil environments., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Starch accounts for up to 90% of the dry weight of rice endosperm and is a key determinant of grain quality. Although starch biosynthesis enzymes have been comprehensively studied, transcriptional regulation of starch-synthesis enzyme-coding genes (SECGs) is largely unknown. In this study, we explored the role of a NAC transcription factor, OsNAC24, in regulating starch biosynthesis in rice. OsNAC24 is highly expressed in developing endosperm. The endosperm of osnac24 mutants is normal in appearance as is starch granule morphology, while total starch content, amylose content, chain length distribution of amylopectin and the physicochemical properties of the starch are changed. In addition, the expression of several SECGs was altered in osnac24 mutant plants. OsNAC24 is a transcriptional activator that targets the promoters of six SECGs; OsGBSSI, OsSBEI, OsAGPS2, OsSSI, OsSSIIIa and OsSSIVb. Since both the mRNA and protein abundances of OsGBSSI and OsSBEI were decreased in the mutants, OsNAC24 functions to regulate starch synthesis mainly through OsGBSSI and OsSBEI. Furthermore, OsNAC24 binds to the newly identified motifs TTGACAA, AGAAGA and ACAAGA as well as the core NAC-binding motif CACG. Another NAC family member, OsNAP, interacts with OsNAC24 and coactivates target gene expression. Loss-of-function of OsNAP led to altered expression in all tested SECGs and reduced the starch content. These results demonstrate that the OsNAC24-OsNAP complex plays key roles in fine-tuning starch synthesis in rice endosperm and further suggest that manipulating the OsNAC24-OsNAP complex regulatory network could be a potential strategy for breeding rice cultivars with improved cooking and eating quality., (© 2023 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.)

Tuberculosis is a very deadly disease, with more than half of all tuberculosis cases dead in countries and regions with relatively poor health care resources. Fortunately, the disease is curable, and early diagnosis and medication can go a long way toward curing TB patients. Unfortunately, traditional methods of TB diagnosis rely on specialist doctors, which is lacking in areas with high TB mortality rates. Diagnostic methods based on artificial intelligence technology are one of the solutions to this problem. We propose a Deep Transferred EfficientNet with SVM (DTE-SVM), which replaces the pre-trained EfficientNet classification layer with an SVM classifier and achieves auspicious performance on a small dataset. After ten runs of 10-fold Cross-Validation, the DTE-SVM has a sensitivity of 93.89±1.96, a specificity of 95.35±1.31, a precision of 95.30±1.24, an accuracy of 94.62±1.00, and an F1-score of 94.62±1.00. In addition, our study conducted ablation studies on the effect of the SVM classifier on model performance and briefly discussed the results.

Protecting haploid pollen and spores against UV-B light and high temperature, 2 major stresses inherent to the terrestrial environment, is critical for plant reproduction and dispersal. Here, we show flavonoids play an indispensable role in this process. First, we identified the flavanone naringenin, which serves to defend against UV-B damage, in the sporopollenin wall of all vascular plants tested. Second, we found that flavonols are present in the spore/pollen protoplasm of all euphyllophyte plants tested and that these flavonols scavenge reactive oxygen species to protect against environmental stresses, particularly heat. Genetic and biochemical analyses showed that these flavonoids are sequentially synthesized in both the tapetum and microspores during pollen ontogeny in Arabidopsis (Arabidopsis thaliana). We show that stepwise increases in the complexity of flavonoids in spores/pollen during plant evolution mirror their progressive adaptation to terrestrial environments. The close relationship between flavonoid complexity and phylogeny and its strong association with pollen survival phenotypes suggest that flavonoids played a central role in the progression of plants from aquatic environments into progressively dry land habitats., Competing Interests: Conflict of interest statement. None declared., (© American Society of Plant Biologists 2023. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Neoadjuvant chemotherapy (NCT) is the standard treatment for patients with locally advanced breast cancer (LABC). The predictive value of heterogeneous circulating tumor cells (CTCs) in NCT response has not been determined. All patients were staged as LABC, and blood samples were collected at the time of biopsy, and after the first and eighth NCT courses. Patients were divided into High responders (High-R) and Low responders (Low-R) according to Miller-Payne system and changes in Ki-67 levels after NCT treatment. A novel SE-i·FISH strategy was applied to detect CTCs. Heterogeneities were successfully analyzed in patients undergoing NCT. Total CTCs increased continuously and were higher in Low-R group, while in High-R group, CTCs increased slightly during NCT before returning to baseline levels. Triploid and tetraploid chromosome 8 increased in Low-R but not High-R group. The number of small CTCs in Low-R group increased significantly until the last sample, however, remained constant in High-R group. The patients with more CTCs had shorter PFS and OS than those with less CTCs after the eighth course of NCT. Total CTCs following NCT could predict patients' responses. More detailed characterizations of CTC blood profiles may improve predictive capacity and treatments of LABC., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: There is a growing trend to apply minimally invasive local treatments for elderly patients with early-stage breast cancer. As a minimally invasive thermal therapy, microwave ablation (MWA) has been attempted to treat breast cancer of small lesions, but its long-term local efficacy on elderly patients has seldom been reported. In this study, we aimed to compare outcomes of MWA combined with endocrine therapy to standard surgery combined with adjuvant therapy in the treatment of hormone receptor (HR)-positive and human epidermal growth factor receptor 2 (HER2)-negative invasive breast cancer for elderly patients., Methods: This prospective multi-center cohort study enrolled patients over 70 years old diagnosed with HR-positive and HER2-negative early-stage invasive breast cancer between January 2016 and July 2021. Patients chose either non-randomized to undergo MWA combined with endocrine therapy (MWA group) or standard surgery combined with adjuvant therapy (surgery group). Endpoints for the comparisons were disease-free survival (DFS), overall survival (OS) and length of hospital stay (LOS) after adjusting for previously reported risk factors using propensity score matching (1:3)., Results: Of the enrolled 132 patients, 33 were in the MWA group and 99 were in the surgery group. MWA was successfully performed in all cases, and technical effectiveness was achieved in all cases. With a median follow-up of 31 months, only one case had local recurrence 23 months after MWA. MWA combined with endocrine therapy and standard surgery combined adjuvant therapy for elderly patients with breast cancer achieved similar DFS [hazard ratio, 0.536; 95% confidence interval (CI): 0.128-2.249] and OS (hazard ratio, 0.537; 95% CI: 0.089-3.235). Besides, MWA had much shorter LOS than standard surgery (7.1 versus 13.0 days, P<0.001)., Conclusions: MWA combined with endocrine therapy and standard surgery combined with adjuvant therapy for elderly breast cancer patients achieved similar outcomes. MWA combined with endocrine may be a feasible treatment strategy for elderly patients with HR-positive and HER2-negative invasive breast cancer., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://gs.amegroups.com/article/view/10.21037/gs-23-33/coif). The authors have no conflicts of interest to declare., (2023 Gland Surgery. All rights reserved.)

Background and Aim: Severe colitis is a common side effect of chemotherapy in cancer patients. In this study, we attempted to enhance the viability of probiotics in a gastric acid environment and improve the colitis induced by dextran sulfate sodium (DSS) and docetaxel., Methods: We purified Lactobacillus from yogurt and estimated their growth at pH 6.8 and pH 2.0. In the further investigation, the bacterial biofilm formation was used to define the mechanism by which administration of Lactobacillus rhamnosus (LGG) via oral gavage alleviates the colitis and intestine permeability of the mice induced by DSS and docetaxel. The potential benefit of probiotics on the treatment of breast cancer metastasis has been assessed as well., Results: Lactobacillus from yogurt growth was unexpectedly faster in the pH 2.0 than in the neutral pH medium during the first hour. LGG administered in the fasting state via oral gavage significantly improved the preventive effect in the colitis caused by DSS and docetaxel. LGG reduced the permeability of the intestine and decreased the expression of proinflammatory cytokines, TNF-α, IL-1β, and IL-6, in colitis by biofilm formation. Increasing the docetaxel dose may reduce breast tumor growth and metastasis in the lung but did not benefit survival due to severe colitis. However, the LGG supplement significantly improved the survival of tumor-bearing mice following a high dose of docetaxel treatment., Conclusions: Our findings provide new insights into the potential mechanism of probiotic protection of the intestine and provide a novel therapeutic strategy to augment the chemotherapeutic treatment of tumors., (© 2023 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.)

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMRI) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians as each scan has many slices of data, and the contrast of it might be low. To address these issues, deep learning (DL) techniques have been employed in the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. The following presents research to detect CVDs using CMR images and the most significant DL methods. Another section discussed the challenges in diagnosing CVDs from CMRI data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. Finally, the most important findings of this study are presented in the conclusion section., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Objective: We aimed to investigate the impact of human epidermal growth factor receptor 2 status (human epidermal growth factor receptor 2-low versus human epidermal growth factor receptor 2-zero) on pathological response to neoadjuvant chemotherapy and survival outcomes in early-stage breast cancer., Methods: Patients with primary invasive breast cancer received neoadjuvant chemotherapy between July 2018 and July 2021 were identified from six hospitals. The primary efficacy end-point was total pathological complete response. The second short-term efficacy end-points include breast pathological complete response, axillary lymph nodes pathological complete response and the score of Miller-Payne grade. Long-term efficacy end-point was disease-free survival., Results: 429 patients with human epidermal growth factor receptor 2 negative invasive tumors were included, 267 (62.24%) had human epidermal growth factor receptor 2-low tumors. Hormone receptor-positive patients had a higher percentage of human epidermal growth factor receptor 2-low tumors compared to hormone receptor-negative patients (71.97% versus 42.14%). The pathological response rate was significantly lower in human epidermal growth factor receptor 2-low tumors than in human epidermal growth factor receptor 2-zero tumors for total patients in univariate analysis, including the rates of total pathological complete response (5.2% versus 14.2%), breast pathological complete response (6.4% versus 17.3%), nodes pathological complete response (26.3% versus 37.7%) and MP4-5 (21.2% versus 33.8%). Subgroup analysis showed that the rates of total pathological complete response, breast pathological complete response and MP4-5 were also significantly lower in human epidermal growth factor receptor 2-low tumors versus human epidermal growth factor receptor 2-zero tumors in both univariate and multivariate analysis in hormone receptor-negative subgroup. With the median follow-up of 24 months, disease-free survival was comparable between these two subgroups (P = 0.816)., Conclusions: Our results demonstrate that human epidermal growth factor receptor 2-low tumors achieved a significantly lower pathological complete response rate with conventional chemotherapy than those with human epidermal growth factor receptor 2-zero tumors, especially for hormone receptor-negative group. Large, randomized, prospective studies are needed to confirm our data and further evaluate the prognostic value of human epidermal growth factor receptor 2-low expression., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Convolutional Neural Networks (CNNs) and the hybrid models of CNNs and Vision Transformers (VITs) are the recent mainstream methods for COVID-19 medical image diagnosis. However, pure CNNs lack global modeling ability, and the hybrid models of CNNs and VITs have problems such as large parameters and computational complexity. These models are difficult to be used effectively for medical diagnosis in just-in-time applications., Methods: Therefore, a lightweight medical diagnosis network CTMLP based on convolutions and multi-layer perceptrons (MLPs) is proposed for the diagnosis of COVID-19. The previous self-supervised algorithms are based on CNNs and VITs, and the effectiveness of such algorithms for MLPs is not yet known. At the same time, due to the lack of ImageNet-scale datasets in the medical image domain for model pre-training. So, a pre-training scheme TL-DeCo based on transfer learning and self-supervised learning was constructed. In addition, TL-DeCo is too tedious and resource-consuming to build a new model each time. Therefore, a guided self-supervised pre-training scheme was constructed for the new lightweight model pre-training., Results: The proposed CTMLP achieves an accuracy of 97.51%, an f1-score of 97.43%, and a recall of 98.91% without pre-training, even with only 48% of the number of ResNet50 parameters. Furthermore, the proposed guided self-supervised learning scheme can improve the baseline of simple self-supervised learning by 1%-1.27%., Conclusion: The final results show that the proposed CTMLP can replace CNNs or Transformers for a more efficient diagnosis of COVID-19. In addition, the additional pre-training framework was developed to make it more promising in clinical practice., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Cerebral microbleed (CMB) is a serious public health concern. It is associated with dementia, which can be detected with brain magnetic resonance image (MRI). CMBs often appear as tiny round dots on MRIs, and they can be spotted anywhere over brain. Therefore, manual inspection is tedious and lengthy, and the results are often short in reproducible. In this paper, a novel automatic CMB diagnosis method was proposed based on deep learning and optimization algorithms, which used the brain MRI as the input and output the diagnosis results as CMB and non-CMB. Firstly, sliding window processing was employed to generate the dataset from brain MRIs. Then, a pre-trained VGG was employed to obtain the image features from the dataset. Finally, an ELM was trained by Gaussian-map bat algorithm (GBA) for identification. Results showed that the proposed method VGG-ELM-GBA provided better generalization performance than several state-of-the-art approaches.

Goal: The purpose of this paper is to recognize autism spectrum disorders (ASD) using graph attention network. Methods: we propose a node features graph attention network (NF-GAT) for learning functional connectivity (FC) features to achieve ASD diagnosis. Firstly, node features are modelled based on functional magnetic resonance imaging (fMRI) data, with each subject modelled as a graph. Next, we use the graph attention layer to learn the node features and gets the node information of different nodes for ASD classification. Results: Compared with other models, the NF-GAT has significant advantages in terms of classification results. Conclusions: NF-GAT can be effectively used for ASD classification., (© 2023 The Authors.)

Problems: For people all over the world, cancer is one of the most feared diseases. Cancer is one of the major obstacles to improving life expectancy in countries around the world and one of the biggest causes of death before the age of 70 in 112 countries. Among all kinds of cancers, breast cancer is the most common cancer for women. The data showed that female breast cancer had become one of the most common cancers., Aims: A large number of clinical trials have proved that if breast cancer is diagnosed at an early stage, it could give patients more treatment options and improve the treatment effect and survival ability. Based on this situation, there are many diagnostic methods for breast cancer, such as computer-aided diagnosis (CAD)., Methods: We complete a comprehensive review of the diagnosis of breast cancer based on the convolutional neural network (CNN) after reviewing a sea of recent papers. Firstly, we introduce several different imaging modalities. The structure of CNN is given in the second part. After that, we introduce some public breast cancer data sets. Then, we divide the diagnosis of breast cancer into three different tasks: 1. classification; 2. detection; 3. segmentation., Conclusion: Although this diagnosis with CNN has achieved great success, there are still some limitations. (i) There are too few good data sets. A good public breast cancer dataset needs to involve many aspects, such as professional medical knowledge, privacy issues, financial issues, dataset size, and so on. (ii) When the data set is too large, the CNN-based model needs a sea of computation and time to complete the diagnosis. (iii) It is easy to cause overfitting when using small data sets., Competing Interests: Conf licts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.

Deep learning has become a primary choice in medical image analysis due to its powerful representation capability. However, most existing deep learning models designed for medical image classification can only perform well on a specific disease. The performance drops dramatically when it comes to other diseases. Generalizability remains a challenging problem. In this paper, we propose an evolutionary attention-based network (EDCA-Net), which is an effective and robust network for medical image classification tasks. To extract task-related features from a given medical dataset, we first propose the densely connected attentional network (DCA-Net) where feature maps are automatically channel-wise weighted, and the dense connectivity pattern is introduced to improve the efficiency of information flow. To improve the model capability and generalizability, we introduce two types of evolution: intra- and inter-evolution. The intra-evolution optimizes the weights of DCA-Net, while the inter-evolution allows two instances of DCA-Net to exchange training experience during training. The evolutionary DCA-Net is referred to as EDCA-Net. The EDCA-Net is evaluated on four publicly accessible medical datasets of different diseases. Experiments showed that the EDCA-Net outperforms the state-of-the-art methods on three datasets and achieves comparable performance on the last dataset, demonstrating good generalizability for medical image classification.

In this work, staggered bottom-gate structure amorphous In-Ga-Zn-O (a-IGZO) thin film transistors (TFTs) with high-k ZrO 2 gate dielectric were fabricated using low-cost atmospheric pressure-plasma enhanced chemical vapor deposition (AP-PECVD) with in situ hydrogenation to modulate the carrier concentration and improve interface quality. Subsequently, a neutral oxygen beam irradiation (NOBI) technique is applied, demonstrating that a suitable NOBI treatment could successfully enhance electrical characteristics by reducing native defect states and minimize the trap density in the back channel. A reverse retrograde channel (RRGC) with ultra-high/low carrier concentration is also formed to prevent undesired off-state leakage current and achieve a very low subthreshold swing. The resulting a-IGZO TFTs exhibit excellent electrical characteristics, including a low subthreshold swing of 72 mV dec -1 and high field-effect mobility of 35 cm 2 V -1 s -1 , due to conduction path passivation and stronger carrier confinement in the RRGC. The UV-vis spectroscopy shows optical transmittance above 90% in the visible range of the electromagnetic spectrum. The study confirms the H 2 plasma with NOBI-treated a-IGZO/ZrO 2 TFT is a promising candidate for transparent electronic device applications., (© 2023 IOP Publishing Ltd.)

Background: Cancer-related fatigue is a widely prevalent global public health concern with serious consequences. Increasing evidence suggests the effectiveness of exercise intervention in treating cancer-related fatigue, but there is a lack of a summary of relevant literature on the same to help reach a clear consensus., Objective: To summarize evidence regarding the efficacy of exercise interventions to reduce cancer fatigue, as determined in systematic reviews (SRs) and/or meta-analyses (MAs)., Method: From inception to September 2022, PubMed (1948-2022), Embase (1974-2022), Cochrane Library (1993-2022), CINAHL (1937-2022), Web of Science (1997-2022), China Knowledge Resource Integrated Database (1999-2022), Wanfang Database (1993-2022), and Chinese Biomedical Database (1994-2022) were searched for inclusion to the study. Two reviewers independently extracted the data from the included articles. AMSTAR II was to evaluate the methodological quality of the reviews., Results: A total of 46 systematic reviews were assessed for data on exercise intervention in reducing cancer-related fatigue among cancer patients. In addition, some studies have reported adverse events during the exercise intervention period. The quality of the included systematic review was found to be low or critically low., Conclusions: The present systematic review of systematic reviews supports exercise intervention for reducing cancer-related fatigue. Further higher-quality studies are warranted to improve the level of evidence for exercise interventions for application in the treatment of cancer-related fatigue., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Brain tumor is one of the common diseases of the central nervous system, with high morbidity and mortality. Due to the wide range of brain tumor types and pathological types, the same type is divided into different subgrades. The imaging manifestations are complex, making clinical diagnosis and treatment difficult. In this paper, we construct SpCaNet (Spinal Convolution Attention Network) to effectively utilize the pathological features of brain tumors, consisting of a Positional Attention (PA) convolution block, Relative self-attention transformer block, and Intermittent fully connected (IFC) layer. Our method is more lightweight and efficient in recognition of brain tumors. Compared with the SOTA model, the number of parameters is reduced by more than three times. In addition, we propose the gradient awareness minimization (GAM) algorithm to solve the problem of insufficient generalization ability of the traditional Stochastic Gradient Descent (SGD) method and use it to train the SpCaNet model. Compared with SGD, GAM achieves better classification performance. According to the experimental results, our method has achieved the highest accuracy of 99.28%, and the proposed method performs well in classifying brain tumors., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

COVID-19 is a positive-sense single-stranded RNA virus caused by a strain of coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several noteworthy variants of SARS-CoV-2 were declared by WHO as Alpha, Beta, Gamma, Delta, and Omicron. Till 13/Dec/2022, it has caused 6.65 million death tolls, and over 649 million confirmed positive cases. Based on the convolutional neural network (CNN), this study first proposes a ten-layer CNN as the backbone model. Then, the exponential linear unit (ELU) is introduced to replace ReLU, and the traditional convolutional block is now transformed into conv-ELU. Finally, an ELU-based CNN (ELUCNN) model is proposed for COVID-19 diagnosis. Besides, the MDA strategy is used to enhance the size of the training set. We develop a mobile app integrating ELUCNN, and this web app is run on a client-server modeled structure. Ten runs of the tenfold cross-validation experiment show our model yields a sensitivity of 94.41 ± 0.98 , a specificity of 94.84 ± 1.21 , an accuracy of 94.62 ± 0.96 , and an F1 score of 94.61 ± 0.95 . The ELUCNN model and mobile app are effective in COVID-19 diagnosis and give better results than 14 state-of-the-art COVID-19 diagnosis models concerning accuracy., Competing Interests: Conflict of interestThe authors declare that the research was conducted without any commercial or financial relationships construed as a potential conflict of interest., (© The Author(s) 2023.)

Rice is a major food crop that sustains approximately half of the world population. Recent worldwide improvements in the standard of living have increased the demand for high-quality rice. Accurate identification of quantitative trait loci (QTLs) for rice grain quality traits will facilitate rice quality breeding and improvement. In the present study, we performed high-resolution QTL mapping for rice grain quality traits using a genotyping-by-sequencing approach. An F 2 population derived from a cross between an elite japonica variety, Koshihikari, and an indica variety, Nona Bokra, was used to construct a high-density genetic map. A total of 3,830 single nucleotide polymorphism markers were mapped to 12 linkage groups spanning a total length of 2,456.4 cM, with an average genetic distance of 0.82 cM. Seven grain quality traits-the percentage of whole grain, percentage of head rice, percentage of area of head rice, transparency, percentage of chalky rice, percentage of chalkiness area, and degree of chalkiness-of the F 2 population were investigated. In total, 15 QTLs with logarithm of the odds (LOD) scores >4 were identified, which mapped to chromosomes 6, 7, and 9. These loci include four QTLs for transparency, four for percentage of chalky rice, four for percentage of chalkiness area, and three for degree of chalkiness, accounting for 0.01%-61.64% of the total phenotypic variation. Of these QTLs, only one overlapped with previously reported QTLs, and the others were novel. By comparing the major QTL regions in the rice genome, several key candidate genes reported to play crucial roles in grain quality traits were identified. These findings will expedite the fine mapping of these QTLs and QTL pyramiding, which will facilitate the genetic improvement of rice grain quality., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Jin, Xu, Yang, Zhang, Wang, Wang, Tao, Hong, Guo, Jia, Song, Leng, Cai and Gao.)

Detection of breast mass plays a very important role in making the diagnosis of breast cancer. For faster detection of breast cancer caused by breast mass, we developed a novel and efficient patch-based breast mass detection system for mammography images. The proposed framework is comprised of three modules, including pre-processing, multiple-level breast tissue segmentation, and final breast mass detection. An improved Deeplabv3+ model for pectoral muscle removal is deployed in pre-processing. We then proposed a multiple-level thresholding segmentation method to segment breast mass and obtained the connected components (ConCs), where the corresponding image patch to each ConC is extracted for mass detection. In the final detection stage, each image patch is classified into breast mass and breast tissue background by trained deep learning models. The patches that are classified as breast mass are then taken as the candidates for breast mass. To reduce the false positive rate in the detection results, we applied the non-maximum suppression algorithm to combine the overlapped detection results. Once an image patch is considered a breast mass, the accurate detection result can then be retrieved from the corresponding ConC in the segmented images. Moreover, a coarse segmentation result can be simultaneously retrieved after detection. Compared to the state-of-the-art methods, the proposed method achieved comparable performance. On CBIS-DDSM, the proposed method achieved a detection sensitivity of 0.87 at 2.86 FPI (False Positive rate per Image), while the sensitivity reached 0.96 on INbreast with an FPI of only 1.29., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Aims: Blood cell classification helps detect various diseases. However, the current classification model of blood cells cannot always get great results. A network that automatically classifies blood cells can provide doctors with data as one of the criteria for diagnosing patients' disease types and severity. If doctors diagnose blood cells, doctors could spend lots of time on the diagnosis. The diagnosis progress is very tedious. Doctors can make some mistakes when they feel tired. On the other hand, different doctors may have different points on the same patient., Methods: We propose a ResNet50-based ensemble of randomized neural networks (ReRNet) for blood cell classification. ResNet50 is used as the backbone model for feature extraction. The extracted features are fed to 3 randomized neural networks (RNNs): Schmidt neural network, extreme learning machine, and dRVFL. The outputs of the ReRNet are the ensemble of these 3 RNNs based on the majority voting mechanism. The 5 × 5-fold cross-validation is applied to validate the proposed network., Results: The average-accuracy, average-sensitivity, average-precision, and average-F1-score are 99.97%, 99.96%, 99.98%, and 99.97%, respectively., Conclusions: The ReRNet is compared with 4 state-of-the-art methods and achieves the best classification performance. The ReRNet is an effective method for blood cell classification based on these results.

Objective: This nonrandomized prospective clinical trial aimed to assess the efficacy, safety and follow-up outcomes of ultrasound-guided high-intensity focused ultrasound (USgHIFU) surgery in patients with breast fibroadenoma., Methods: With the approval of the institutional ethics committee and written informed consent, a total of 113 patients diagnosed with breast fibroadenoma by core-needle biopsy in our hospital were recruited. USgHIFU surgery was performed under local anesthesia. Contrast-enhanced ultrasound (CEUS) or contrast-enhanced MRI (CEMRI) was performed to evaluate the nonperfused volume (NPV). The patients were followed up with physical examination and ultrasound imaging., Results: The clinical outcome of 85 patients with 147 fibroadenomas with a follow-up time of more than 3 months was analyzed in this study. Fifty-two patients had one lesion, twenty-one patients had two lesions and twelve patients had more than two lesions. During USgHIFU, the median localization time for all fibroadenomas was 3 (interquartile range: 1, 5) min, and the median treatment time was 9 (interquartile range: 5, 15) min. Under local anesthesia, all the patients tolerated the treatment well. No serious epidermal burns were observed in any of the patients. Based on CEUS or CEMRI imaging evaluation, the median NPV ratio was 100% (interquartile range: 79.2%, 116.8%). The VRR were 26.77 ± 50.05%, 50.22 ± 42.01% and 72.74 ± 35.39% at 3-6 months, 6-12 months and >12 months, respectively, which showed significant statistical difference ( p  < .001)., Conclusion: Ultrasound-guided HIFU surgery is an effective and safe noninvasive alternative technique for the treatment of breast fibroadenoma.

Adequately choosing the physicochemical characteristics of adsorbent is crucial in improving its adsorption performance. This work investigated the effect of particle size of magnetic resins on adsorption behaviors of tetracycline (TC) and their antifouling performance. Smaller particle size resin Q150 (10-30 μm) shows notably faster TC adsorption kinetics when compared resins with hundreds of microns (Q100 and Q1). Simulated by Weber-Morris equation, the film diffusion time of Q150 was only 20 min, 2-25 times faster than that of other resins. At this adsorption time, Q150 can reach more than 80% of the maximum adsorption, and the ring-like fluorescence images indicate that the molecules are accumulated on the external surface. Q150 also shows better reusability and antifouling performance over Q100 and Q1. After 20 adsorption-desorption cycles, the adsorption capacity of Q150 at 20 min only decreases 9.7%. The presence of tannic acid also only slightly decreases the adsorption capacity. The faster adsorption kinetics and the superior antifouling performance of Q150 make it a promising adsorbent in future use., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Chemoresistance is a major obstacle in the neoadjuvant chemotherapy (NCT) of locally advanced breast cancer (LABC). Identification of miRNAs as prognostic biomarkers may help overcome chemoresistance of breast cancer (BC). This study aimed to evaluate the expression level of miR-1275 in plasma samples and its biological functions in the chemoresistance of BC. The expression levels of miR-1275 in plasma samples and cells were measured by RT-qPCR. CRISPR/Cas9-mediated gene editing was used to construct miR-1275 knock-out cells in MCF-7. We found that miR-1275 was significantly downregulated in plasma from patients resistant to chemotherapy and in chemoresistant BC cell lines, while patients with low levels of miR-1275 showed poor overall survival. miR-1275 knock-out promoted chemoresistance in BC cells by increasing the properties of cancer stem cells (CSCs). Mechanistically, we identified that MDK was determined to be direct downstream protein of miR-1275 which initiated PI3K/Akt signaling in breast cancer cells. We demonstrated that the high expression level of miR-1275 in plasma predicted better response to NCT. The reduction of miR-1275 promoted BC cells chemoresistance by increasing CSCs properties via targeting MDK/AKT axis. The potential of miR-1275 as a new prognostic biomarker and therapeutic target of BC patients was identified., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Community-acquired pneumonia (CAP) is considered a sort of pneumonia developed outside hospitals and clinics. To diagnose community-acquired pneumonia (CAP) more efficiently, we proposed a novel neural network model. We introduce the 2-dimensional wavelet entropy (2d-WE) layer and an adaptive chaotic particle swarm optimization (ACP) algorithm to train the feed-forward neural network. The ACP uses adaptive inertia weight factor (AIWF) and Rossler attractor (RA) to improve the performance of standard particle swarm optimization. The final combined model is named WE-layer ACP-based network (WACPN), which attains a sensitivity of 91.87±1.37%, a specificity of 90.70±1.19%, a precision of 91.01±1.12%, an accuracy of 91.29±1.09%, F1 score of 91.43±1.09%, an MCC of 82.59±2.19%, and an FMI of 91.44±1.09%. The AUC of this WACPN model is 0.9577. We find that the maximum deposition level chosen as four can obtain the best result. Experiments demonstrate the effectiveness of both AIWF and RA. Finally, this proposed WACPN is efficient in diagnosing CAP and superior to six state-of-the-art models. Our model will be distributed to the cloud computing environment., Competing Interests: Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.

Since 2019, the coronavirus disease-19 (COVID-19) has been spreading rapidly worldwide, posing an unignorable threat to the global economy and human health. It is a disease caused by severe acute respiratory syndrome coronavirus 2, a single-stranded RNA virus of the genus Betacoronavirus. This virus is highly infectious and relies on its angiotensin-converting enzyme 2-receptor to enter cells. With the increase in the number of confirmed COVID-19 diagnoses, the difficulty of diagnosis due to the lack of global healthcare resources becomes increasingly apparent. Deep learning-based computer-aided diagnosis models with high generalisability can effectively alleviate this pressure. Hyperparameter tuning is essential in training such models and significantly impacts their final performance and training speed. However, traditional hyperparameter tuning methods are usually time-consuming and unstable. To solve this issue, we introduce Particle Swarm Optimisation to build a PSO-guided Self-Tuning Convolution Neural Network (PSTCNN), allowing the model to tune hyperparameters automatically. Therefore, the proposed approach can reduce human involvement. Also, the optimisation algorithm can select the combination of hyperparameters in a targeted manner, thus stably achieving a solution closer to the global optimum. Experimentally, the PSTCNN can obtain quite excellent results, with a sensitivity of 93.65%±1.86%, a specificity of 94.32%±2.07%, a precision of 94.30%±2.04%, an accuracy of 93.99%±1.78%, an F1-score of 93.97%±1.78%, Matthews Correlation Coefficient of 87.99%±3.56%, and Fowlkes-Mallows Index of 93.97%±1.78%. Our experiments demonstrate that compared to traditional methods, hyperparameter tuning of the model using an optimisation algorithm is faster and more effective., Competing Interests: Conflicts of Interest The authors declare that they have no conflicts of interest to report regarding the present study.

With a global COVID-19 pandemic, the number of confirmed patients increases rapidly, leaving the world with very few medical resources. Therefore, the fast diagnosis and monitoring of COVID-19 are one of the world's most critical challenges today. Artificial intelligence-based CT image classification models can quickly and accurately distinguish infected patients from healthy populations. Our research proposes a deep learning model (WE-SAJ) using wavelet entropy for feature extraction, two-layer FNNs for classification and the adaptive Jaya algorithm as a training algorithm. It achieves superior performance compared to the Jaya-based model. The model has a sensitivity of 85.47±1.84, specificity of 87.23±1.67 precision of 87.03±1.34, an accuracy of 86.35±0.70, and F1 score of 86.23±0.77, Matthews correlation coefficient of 72.75±1.38, and Fowlkes-Mallows Index of 86.24±0.76. Our experiments demonstrate the potential of artificial intelligence techniques for COVID-19 diagnosis and the effectiveness of the Self-adaptive Jaya algorithm compared to the Jaya algorithm for medical image classification tasks.

The COVID-19 virus's rapid global spread has caused millions of illnesses and deaths. As a result, it has disastrous consequences for people's lives, public health, and the global economy. Clinical studies have revealed a link between the severity of COVID-19 cases and the amount of virus present in infected people's lungs. Imaging techniques such as computed tomography (CT) and chest x-rays can detect COVID-19 (CXR). Manual inspection of these images is a difficult process, so computerized techniques are widely used. Deep convolutional neural networks (DCNNs) are a type of machine learning that is frequently used in computer vision applications, particularly in medical imaging, to detect and classify infected regions. These techniques can assist medical personnel in the detection of patients with COVID-19. In this article, a Bayesian optimized DCNN and explainable AI-based framework is proposed for the classification of COVID-19 from the chest X-ray images. The proposed method starts with a multi-filter contrast enhancement technique that increases the visibility of the infected part. Two pre-trained deep models, namely, EfficientNet-B0 and MobileNet-V2, are fine-tuned according to the target classes and then trained by employing Bayesian optimization (BO). Through BO, hyperparameters have been selected instead of static initialization. Features are extracted from the trained model and fused using a slicing-based serial fusion approach. The fused features are classified using machine learning classifiers for the final classification. Moreover, visualization is performed using a Grad-CAM that highlights the infected part in the image. Three publically available COVID-19 datasets are used for the experimental process to obtain improved accuracies of 98.8, 97.9, and 99.4%, respectively., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Hamza, Attique Khan, Wang, Alhaisoni, Alharbi, Hussein, Alshazly, Kim and Cha.)

Aim: Corona Virus Disease 2019 (COVID-19) was a lung disease with high mortality and was highly contagious. Early diagnosis of COVID-19 and distinguishing it from pneumonia was beneficial for subsequent treatment., Objectives: Recently, Graph Convolutional Network (GCN) has driven a significant contribution to disease diagnosis. However, limited by the nature of the graph convolution algorithm, deep GCN has an over-smoothing problem. Most of the current GCN models are shallow neural networks, which do not exceed five layers. Furthermore, the objective of this study is to develop a novel deep GCN model based on the DenseGCN and the pre-trained model of deep Convolutional Neural Network (CNN) to complete the diagnosis of chest X-ray (CXR) images., Methods: We apply the pre-trained model of deep CNN to perform feature extraction on the data to complete the extraction of pixel-level features in the image. And then, to extract the potential relationship between the obtained features, we propose Neighbourhood Feature Reconstruction Algorithm to reconstruct them into graph-structured data. Finally, we design a deep GCN model that exploits the graph-structured data to diagnose COVID-19 effectively. In the deep GCN model, we propose a Node-Self Convolution Algorithm (NSC) based on feature fusion to construct a deep GCN model called NSCGCN (Node-Self Convolution Graph Convolutional Network)., Results: Experiments were carried out on the Computed Tomography (CT) and CXR datasets. The results on the CT dataset confirmed that: compared with the six state-of-the-art (SOTA) shallow GCN models, the accuracy and sensitivity of the proposed NSCGCN had improve 8% as sensitivity (Sen.) = 87.50%, F1 score = 97.37%, precision (Pre.) = 89.10%, accuracy (Acc.) = 97.50%, area under the ROC curve (AUC) = 97.09%. Moreover, the results on the CXR dataset confirmed that: compared with the fourteen SOTA GCN models, sixteen SOTA CNN transfer learning models and eight SOTA COVID-19 diagnosis methods on the COVID-19 dataset. Our proposed method had best performances as Sen. = 96.45%, F1 score = 96.45%, Pre. = 96.61%, Acc. = 96.45%, AUC = 99.22%., Conclusion: Our proposed NSCGCN model is effective and performed better than the thirty-eight SOTA methods. Thus, the proposed NSC could help build deep GCN models. Our proposed COVID-19 diagnosis method based on the NSCGCN model could help radiologists detect pneumonia from CXR images and distinguish COVID-19 from Ordinary Pneumonia (OPN). The source code of this work will be publicly available at https://github.com/TangChaosheng/NSCGCN., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The concentrations of six priority phthalate esters (PAEs) in 700 soil samples and 110 sediment samples from an area in China containing plastic solid waste (PSW) recycling sites were determined. The total concentrations of the six PAEs in soil and sediment were not detected - 274 and not detected - 597 mg kg -1 , respectively, and the mean concentrations in soil and sediment were 14.4 and 31.7 mg kg -1 , respectively. The dominant PAEs were di(2-ethylhexyl) phthalate and di-n-butyl phthalate. PAEs were detected in soil collected from the surface to 0.5 m below ground level around the PSW recycling sites, and the concentrations were markedly higher in these areas than at other polluted sites. PSW recycling is an important source of PAEs to soil and sediment. The di(2-ethylhexyl)phthalate concentrations in soil were higher than the relevant concentrations that pose environmental risks for sensitive land uses and non-sensitive land uses (42 and 121 mg kg -1 , respectively), indicating emissions of PAEs from PSW recycling sites may pose environmental risks. The results indicate that PAE pollution at PSW sites needs to be better controlled and managed., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: CD161 has been identified as a prognostic biomarker in many neoplasms, but its role in breast cancer (BC) has not been fully explained. We aimed to investigate the molecular mechanism and prognostic value of CD161 in BC. Methods: CD161 expression profile was extracted from TIMER, Oncomine, UALCAN databases, and verified by the Gene Expression Omnibus (GEO) database and quantitative real-time polymerase chain reaction (qRT-PCR). The prognostic value of CD161 was assessed via GEPIA, Kaplan-Meier plotter and PrognoScan databases. The Cox regression and nomogram analyses were conducted to further validate the association between CD161 expression and survival. Gene set enrichment analysis (GSEA), Gene Ontology (GO) analysis, and KEGG pathway enrichment analysis were performed to probe the tumor-associated annotations of CD161 . CIBERSORT and ssGSEA were employed to investigate the correlation between CD161 expression and immune cell infiltration in BC, and the result was verified by TIMER and TISIDB. Results: Multiple BC cohorts showed that CD161 expression was decreased in BC, and a high CD161 expression was associated with a preferable prognosis. Therefore, we identified the combined model including CD161 , age and PR status to predict the survival (C index = 0.78) of BC patients. Functional enrichment analysis indicated that CD161 and its co-expressed genes were closely related to several cancerous and immune signaling pathways, suggesting its involvement in immune response during cancer development. Moreover, immune infiltration analysis revealed that CD161 expression was correlated with immune infiltration. Conclusion: Collectively, our findings revealed that CD161 may serve as a potential biomarker for favorable prognosis and a promising immune therapeutic target in BC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Weng, Xie, Zheng, Hou, Wang and Huang.)

Purpose: To investigate the correlation between pre-ablation ultrasound radiomics features and the sonication energy for focused ultrasound surgery (FUS) of benign breast tumors. Method: 53 benign breast tumors of 28 patients treated by ultrasound-guided focused ultrasound surgery (USgFUS) were included in this study. The sonication energy per unit volume of each tumor was calculated. Three-quarter point was chosen as the cut-off to divide the 53 included tumors into high sonication energy (HSE, n = 14) and low sonication energy (LSE, n = 39) groups. For each tumor, the region of interest (ROI) of both the tumor itself (tROI) and the near field tissue (nfROI) were delineated and analyzed separately using ImageJ software. Pearson correlation coefficient and multiple linear regression analysis were used for radiomics feature selection. To explore the diagnostic performance of different ultrasound radiomics features, a receiver operating characteristic (ROC) curve analysis was performed. Results: In total of 68 radiomics features were extracted from pre-ablation ultrasound images of each tumor. Of all radiomics features, BX in tROI ( p < 0.001), BX ( p = 0.001) and Circ ( p = 0.019) in nfROI were independently predictive features of sonication energy per unit volume. The ROC curves showed that the area under the curve (AUC) values of BX in tROI, BX, and Circ in nfROI were 0.797, 0.787 and 0.822, respectively. Conclusion: This study provided three radiomics features of pre-ablation ultrasound image as predictors of sonication dose for FUS in benign breast tumors. Further clinical trials are needed to confirm the predictive effect of these radiomics features., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Liang, Zhang, Xia, Chen, Wang, Weng, Xie, Chen, Liu and Wang.)