OsMPK12 positively regulates rice blast resistance via OsEDC4-mediated transcriptional regulation of immune-related genes.

Mitogen-activated protein kinase (MAPK) signalling cascades are functionally important signalling modules in eukaryotes. Transcriptome reprogramming of immune-related genes is a key process in plant immunity. Emerging evidence shows that plant MAPK cascade is associated with processing (P)-body components and contributes to transcriptome reprogramming of immune-related genes. However, it remains largely unknown how this process is regulated. Here, we show that OsMPK12, which is induced by Magnaporthe oryzae infection, positively regulates rice blast resistance. Further analysis revealed that OsMPK12 directly interacts with enhancer of mRNA decapping protein 4 (OsEDC4), a P-body-located protein, and recruits OsEDC4 to where OsMPK12 is enriched. Importantly, OsEDC4 directly interacts with two decapping complex members OsDCP1 and OsDCP2, indicating that OsEDC4 is a subunit of the mRNA decapping complex. Additionally, we found that OsEDC4 positively regulates rice blast resistance by regulating expression of immune-related genes and maintaining proper mRNA levels of some negatively-regulated genes. And OsMPK12 and OsEDC4 are also involved in rice growth and development regulation. Taken together, our data demonstrate that OsMPK12 positively regulates rice blast resistance via OsEDC4-mediated mRNA decay of immune-related genes, providing new insight into not only the new role of the MAPK signalling cascade, but also posttranscriptional regulation of immune-related genes.

Clinical efficacy of tumor organoid-guided cancer therapy for locally advanced unresectable or metastatic breast cancer.

Patient-derived organoids (PDOs) may facilitate treatment selection. This retrospective cohort study evaluated the feasibility and clinical benefit of using PDOs to guide personalized treatment in metastatic breast cancer (MBC). Patients diagnosed with MBC were recruited between January 2019 and August 2022. PDOs were established and the efficacy of customized drug panels was determined by measuring cell mortality after drug exposure. Patients receiving organoid-guided treatment (OGT) were matched 1:2 by nearest neighbor propensity scores with patients receiving treatment of physician's choice (TPC). The primary outcome was progression-free survival. Secondary outcomes included objective response rate and disease control rate. Targeted gene sequencing and pathway enrichment analysis were performed. Forty-six PDOs (46 of 51, 90.2%) were generated from 45 MBC patients. PDO drug screening showed an accuracy of 78.4% (95% CI 64.9%-91.9%) in predicting clinical responses. Thirty-six OGT patients were matched to 69 TPC patients. OGT was associated with prolonged median progression-free survival (11.0 months vs. 5.0 months; hazard ratio 0.53 [95% CI 0.33-0.85]; p = .01) and improved disease control (88.9% vs. 63.8%; odd ratio 4.26 [1.44-18.62]) compared with TPC. The objective response rate of both groups was similar. Pathway enrichment analysis in hormone receptor-positive, human epidermal growth factor receptor 2-negative patients demonstrated differentially modulated pathways implicated in DNA repair and transcriptional regulation in those with reduced response to capecitabine/gemcitabine, and pathways associated with cell cycle regulation in those with reduced response to palbociclib. Our study shows that PDO-based functional precision medicine is a feasible and effective strategy for MBC treatment optimization and customization.

Noninvasive Artificial Intelligence System for Early Predicting Residual Cancer Burden during Neoadjuvant Chemotherapy in Breast Cancer.

OBJECTIVE: To develop an artificial intelligence (AI) system for the early prediction of residual cancer burden (RCB) scores during neoadjuvant chemotherapy (NAC) in breast cancer. SUMMARY BACKGROUND DATA: RCB III indicates drug resistance in breast cancer, and early detection methods are lacking. METHODS: This study enrolled 1048 patients with breast cancer from four institutions, who were all receiving NAC. Magnetic resonance images were collected at the pre- and mid-NAC stages, and radiomics and deep learning features were extracted. A multitask AI system was developed to classify patients into three groups (RCB 0-I, II, and III ) in the primary cohort (PC, n=335). Feature selection was conducted using the Mann-Whitney U- test, Spearman analysis, least absolute shrinkage and selection operator regression, and the Boruta algorithm. Single-modality models were developed followed by model integration. The AI system was validated in three external validation cohorts. (EVCs, n=713). RESULTS: Among the patients, 442 (42.18%) were RCB 0-I, 462 (44.08%) were RCB II and 144 (13.74%) were RCB III. Model-I achieved an area under the curve (AUC) of 0.975 in the PC and 0.923 in the EVCs for differentiating RCB III from RCB 0-II. Model-II distinguished RCB 0-I from RCB II-III, with an AUC of 0.976 in the PC and 0.910 in the EVCs. Subgroup analysis confirmed that the AI system was consistent across different clinical T stages and molecular subtypes. CONCLUSIONS: The multitask AI system offers a noninvasive tool for the early prediction of RCB scores in breast cancer, supporting clinical decision-making during NAC.

LncRNA-PCat19 acts as a ceRNA of miR-378a-3p to facilitate microglia activation and accelerate chronic neuropathic pain in rats by promoting KDM3A-mediated BDNF demethylation.

The pathogenesis of neuropathic pain (NP) is complex, and there are various pathological processes. Previous studies have suggested that lncRNA PCAT19 is abnormally expressed in NP conduction and affects the occurrence and development of pain. The aim of this study is to analyze the role and mechanism of PCAT19 in NP induced by chronic compressive nerve injury (CCI) in mice. In this study, C57BL/6 mice were applied to establish the CCI model. sh-PCAT19 was intrathecally injected once a day for 5 consecutive days from the second day after surgery. We discovered that PCat19 level was gradually up-regulated with the passage of modeling time. Downregulation of Iba-1-positive expression, M1/M2 ratio of microglia, and pro-inflammatory factors in the spinal cords of CCI-mice after PCat19 knock-downed was observed. Mechanically, the expression of miR-378a-3p was negatively correlated with KDM3A and PCat19. Deletion of KDM3A prevented H3K9me2 demethylation of BDNF promoter and suppressed BDNF expression. Further, KDM3A promotes CCI-induced neuroinflammation and microglia activation by mediating Brain-derived neurotrophic factor (BDNF) demethylation. Together, the results suggest that PCat19 may be involved in the development of NP and that PCat19 shRNA injection can attenuate microglia-induced neuroinflammation by blocking KDM3A-mediated demethylation of BDNF and BDNF release.

Expression of Staphylococcus aureus translation elongation factor P is regulated by a stress-inducible promotor.

Translation elongation factor P, expressed by the efp gene, is a conserved protein closely related to bacterial virulence and environmental stress regulation responses, however, little is known about the efp gene expression regulations. Here, the strain of Staphylococcus aureus subsp. aureus NCTC 8325 was taken as the research object and cultured under different conditions, including different culture temperatures, pH, and antibiotics, to study the expression of the efp gene in S. aureus by qRT-PCR, the results showed that the expression of the efp gene is upregulated under high temperature (40 °C), acidic (pH 5.4) or alkaline (pH 9.4) culture conditions, but upregulated early and downregulated later under the conditions of 0.5 MIC antibiotics (chloramphenicol at the final concentration of 2 µg/mL and vancomycin at the final concentration of 0.25 µg/mL), indicating that the efp promoter in S. aureus is inducible. The efp promoter sequence and structure in S. aureus were predicted by bioinformatics methods, and the predicted promoter was validated by constructing a promoter-probe vector and a series of promoter mutants, the results showed that the efp promoter sequence in S. aureus, named Pro, located in 1,548,179-1,548,250 of the S. aureus genome (NC_007795.1), and the sequence of - 10 element is CCTTATAGT, - 35 element is TTTACT. The results above could lay a foundation for screening transcription factors involved in the expression of the efp gene and then exploring the transcriptional regulation mechanism of EF-P in S. aureus.

Consistency analysis of consciousness index and bispectral index in monitoring the depth of sevoflurane anesthesia in laparoscopic surgery.

Background: The Index of Consciousness (IoC) is a new monitoring index of anesthesia depth reflecting the state of consciousness of the brain independently developed by China. The research on monitoring the depth of anesthesia mainly focuses on propofol, and bispectral index (BIS) is a sensitive and accurate objective index to evaluate the state of consciousness at home and abroad. This study mainly analyzed the effect of IoC on monitoring the depth of sevoflurane anesthesia and the consistency and accuracy with BIS when monitoring sevoflurane maintenance anesthesia. Objective: To investigate the monitoring value of the Index of Consciousness (IoC) for the depth of sevoflurane anesthesia in laparoscopic surgery. Methods: The study population consisted of 108 patients who experienced elective whole-body anesthesia procedures within the timeframe of April 2020 to June 2023 at our hospital. Throughout the anesthesia process, which encompassed induction and maintenance using inhaled sevoflurane, all patients were diligently monitored for both the Bispectral Index (BIS) and the Index of Consciousness (IoC). We conducted an analysis to assess the correlation between IoC and BIS throughout the anesthesia induction process and from the maintenance phase to the regaining of consciousness. To evaluate the predictive accuracy of IoC and BIS for the onset of unconsciousness during induction and the return of consciousness during emergence, we employed receiver operating characteristic (ROC) curve analysis. Results: The mean difference between BIS and IoC, spanning from the pre-anesthesia induction phase to the completion of propofol induction, was 1.3 (95% Limits of Agreement [-53.4 to 56.0]). Similarly, during the interval from the initiation of sevoflurane inhalation to the point of consciousness restoration, the average difference between BIS and IoC was 0.3 (95% LOA [-10.8 to 11.4]). No statistically significant disparities were observed in the data acquired from the two measurement methodologies during both the anesthesia induction process and the journey from maintenance to the regaining of consciousness (P > 0.05). The outcomes of the ROC curve analysis disclosed that the areas under the curve (AUC) for prognosticating the occurrence of loss of consciousness were 0.967 (95% CI [0.935-0.999]) for BIS and 0.959 (95% CI [0.924-0.993]) for IoC, with optimal threshold values set at 81 (sensitivity: 88.10%, specificity: 92.16%) and 77 (sensitivity: 79.55%, specificity: 95.45%) correspondingly. For the prediction of recovery of consciousness, the AUCs were 0.995 (95% CI [0.987-1.000]) for BIS and 0.963 (95% CI [0.916-1.000]) for IoC, each associated with optimal cutoff values of 76 (sensitivity: 92.86%, specificity: 100.00%) and 72 (sensitivity: 86.36%, specificity: 100.00%) respectively. Conclusion: The monitoring of sevoflurane anesthesia maintenance using IoC demonstrates a level of comparability to BIS, and its alignment with BIS during the maintenance phase of sevoflurane anesthesia is robust. IoC displays promising potential for effectively monitoring the depth of anesthesia.

High altitude retinopathy: An overview and new insights.

High altitude retinopathy (HAR) is a common ocular disorder that occurs on ascent to high altitude. There are many clinical symptoms, retinal vascular dilatation, retinal edema and hemorrhage are common. These usually do not or slightly affect vision; rarely, severe cases develop serious or permanent vision loss. At present, the research progress of HAR mainly focuses on hemodynamic changes, blood-retinal barrier damage, oxidative stress and inflammatory response. Although the related studies on HAR are limited, it shows that HAR still belongs to hypoxia, and hypobaric hypoxia plays an aggravating role in promoting the development of the disease. Various studies have demonstrated the correlation of HAR with acute mountain sickness (AMS) and high-altitude cerebral edema (HACE), so a deeper understanding of HAR is important. The slow ascent rates and ascent altitude are the key to preventing any altitude sickness. Research on traditional chinese medicine (TCM) and western medicine has been gradually carried out. Further exploration of the pathogenesis and prevention strategies of HAR will provide better guidance for doctors and high-altitude travelers.

Mechanism of Circ_HECW2 regulating osteoblast apoptosis in osteoporosis by attenuating the maturation of miR-1224-5p.

BACKGROUND: Osteoporosis (OP) poses a significant clinical challenge with escalating morbidity. This study explores Circ_HECW2 expression in OP patients and its regulatory role in lipopolysaccharide (LPS)-induced osteoblast apoptosis. METHODS: Circ_HECW2 expression in OP patient serum and healthy controls was quantified using RT-qPCR. Diagnostic value of Circ_HECW2 for OP was assessed via ROC curve. Pearson's correlation model examined associations between indicators. Human osteoblasts HFOB1.19, treated with LPS, were analyzed for Circ_HECW2, pre-miR-1224, miR-1224-5p, and PDK2 mRNA levels. TUNEL assay determined cell apoptosis and Western blot assessed cleaved-caspase-3 protein levels. RNase R resistance assay and actinomycin D assay confirmed Circ_HECW2's cyclic structure. RNA pull-down and dual-luciferase reporter assay verified binding relationships between Circ_HECW2 and miR-1224 and between miR-1224-5p and PDK2. RESULTS: Circ_HECW2 exhibited elevated expression in OP patients with diagnostic significance and a negative correlation with lumbar T-score. LPS co-culture increased Circ_HECW2 expression in HFOB1.19 cells, significantly elevating apoptosis index and cleaved-caspase-3. Circ_HECW2 downregulation inhibited HFOB1.19 apoptosis, reduced pre-miR-1224 expression, and elevated mature miR-1224-5p. Circ_HECW2 bound to pre-miR-1224, and inhibiting miR-1224-5p reversed the effect of Circ_HECW2 downregulation on osteoblast apoptosis. miR-1224-5p targeted PDK2 transcription. CONCLUSION: Circ_HECW2, highly expressed in OP, holds diagnostic significance and reflects disease severity. Circ_HECW2 reduces mature miR-1224-5p by binding to pre-miR-1224, upregulating PDK2, and facilitating LPS-induced osteoblast apoptosis.

Neoadjuvant Chemotherapy and Neoadjuvant Chemotherapy With Immunotherapy Result in Different Tumor Shrinkage Patterns in Triple-Negative Breast Cancer.

PURPOSE: This study aims to explore whether neoadjuvant chemotherapy with immunotherapy (NACI) leads to different tumor shrinkage patterns, based on magnetic resonance imaging (MRI), compared to neoadjuvant chemotherapy (NAC) alone in patients with triple-negative breast cancer (TNBC). Additionally, the study investigates the relationship between tumor shrinkage patterns and treatment efficacy was investigated. METHODS: This retrospective study included patients with TNBC patients receiving NAC or NACI from January 2019 until July 2021 at our center. Pre- and post-treatment MRI results were obtained for each patient, and tumor shrinkage patterns were classified into three categories as follows: 1) concentric shrinkage (CS); 2) diffuse decrease; and 3) no change. Tumor shrinkage patterns were compared between the NAC and NACI groups, and the relevance of the patterns to treatment efficacy was assessed. RESULTS: Of the 99 patients, 65 received NAC and 34 received NACI. The CS pattern was observed in 53% and 20% of patients in the NAC and NACI groups, respectively. Diffuse decrease pattern was observed in 36% and 68% of patients in the NAC and NACI groups. The association between the treatment regimens (NAC and NACI) and tumor shrinkage patterns was statistically significant (p = 0.004). The postoperative pathological complete response (pCR) rate was 45% and 82% in the NAC and NACI groups (p < 0.001), respectively. In the NACI group, 17% of patients with the CS pattern and 56% of those with the diffuse decrease pattern achieved pCR (p = 0.903). All tumor shrinkage patterns were associated with achieved a high pCR rate in the NACI group. CONCLUSION: Our study demonstrates that the diffuse decrease pattern of tumor shrinkage is more common following NACI than that following NAC. Furthermore, our findings suggest that all tumor shrinkage patterns are associated with a high pCR rate in patients with TNBC treated with NACI. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04909554.

18F-FDG PET/CT-based deep learning radiomics predicts 5-years disease-free survival after failure to achieve pathologic complete response to neoadjuvant chemotherapy in breast cancer.

BACKGROUND: This study aimed to assess whether a combined model incorporating radiomic and depth features extracted from PET/CT can predict disease-free survival (DFS) in patients who failed to achieve pathologic complete response (pCR) after neoadjuvant chemotherapy. RESULTS: This study retrospectively included one hundred and five non-pCR patients. After a median follow-up of 71 months, 15 and 7 patients experienced recurrence and death, respectively. The primary tumor volume underwent feature extraction, yielding a total of 3644 radiomic features and 4096 depth features. The modeling procedure employed Cox regression for feature selection and utilized Cox proportional-hazards models to make predictions on DFS. Time-dependent receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were utilized to evaluate and compare the predictive performance of different models. 2 clinical features (RCB, cT), 4 radiomic features, and 7 depth features were significant predictors of DFS and were included to develop models. The integrated model incorporating RCB, cT, and radiomic and depth features extracted from PET/CT images exhibited the highest accuracy for predicting 5-year DFS in the training (AUC 0.943) and the validation cohort (AUC 0.938). CONCLUSION: The integrated model combining radiomic and depth features extracted from PET/CT images can accurately predict 5-year DFS in non-pCR patients. It can help identify patients with a high risk of recurrence and strengthen adjuvant therapy to improve survival.

Lanatoside C decelerates proliferation and induces apoptosis through inhibition of STAT3 and ROS-mediated mitochondrial membrane potential transformation in cholangiocarcinoma.

Introduction: The incidence of cholangiocarcinoma (CCA) has increased worldwide in recent years. Given the poor prognosis associated with the current management approach of CCA, new therapeutic agents are warranted to improve the prognosis of this patient population. Methods: In this study, we extracted five cardiac glycosides (CGs) from natural plants: digoxin, lanatoside A, lanatoside C, lanatoside B, and gitoxin. Follow-up experiments were performed to assess the effect of these five extracts on cholangiocarcinoma cells and compounds with the best efficacy were selected. Lanatoside C (Lan C) was selected as the most potent natural extract for subsequent experiments. We explored the potential mechanism underlying the anticancer activity of Lan C on cholangiocarcinoma cells by flow cytometry, western blot, immunofluorescence, transcriptomics sequencing, network pharmacology and in vivo experiments. Results: We found that Lan C time-dependently inhibited the growth and induced apoptosis of HuCCT-1 and TFK-1 cholangiocarcinoma cells. Besides Lan C increased the reactive oxygen species (ROS) content in cholangiocarcinoma cells, decreased the mitochondrial membrane potential (MMP) and resulted in apoptosis. Besides, Lan C downregulated the protein expression of STAT3, leading to decreased expression of Bcl-2 and Bcl-xl, increased expression of Bax, activation of caspase-3, and initiation of apoptosis. N-acetyl-L-cysteine (NAC) pretreatment reversed the effect of Lan C. In vivo, we found that Lan C inhibited the growth of cholangiocarcinoma xenografts without toxic effects on normal cells. Tumor immunohistochemistry showed that nude mice transplanted with human cholangiocarcinoma cells treated with Lan C exhibited decreased STAT3 expression and increased caspase-9 and caspase-3 expression in tumors, consistent with the in vitro results. Conclusion: In summary, our results substantiates that cardiac glycosides have strong anti-CCA effects. Interestingly the biological activity of Lan C provides a new anticancer candidate for the treatment of cholangiocarcinoma.

Orthogonal Combinatorial Raman Codes Enable Rapid High-Throughput-Out Library Screening of Cell-Targeting Ligands.

High-throughput assays play an important role in the fields of drug discovery, genetic analysis, and clinical diagnostics. Although super-capacity coding strategies may facilitate labeling and detecting large numbers of targets in a single assay, practically, the constructed large-capacity codes have to be decoded with complicated procedures or are lack of survivability under the required reaction conditions. This challenge results in either inaccurate or insufficient decoding outputs. Here, we identified chemical-resistant Raman compounds to build a combinatorial coding system for the high-throughput screening of cell-targeting ligands from a focused 8-mer cyclic peptide library. The accurate in situ decoding results proved the signal, synthetic, and functional orthogonality for this Raman coding strategy. The orthogonal Raman codes allowed for a rapid identification of 63 positive hits at one time, evidencing a high-throughput-out capability in the screening process. We anticipate this orthogonal Raman coding strategy being generalized to enable efficient high-throughput-out screening of more useful ligands for cell targeting and drug discovery.

Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: a multicenter, retrospective study.

Background: Accurate identification of pCR to neoadjuvant chemotherapy (NAC) is essential for determining appropriate surgery strategy and guiding resection extent in breast cancer. However, a non-invasive tool to predict pCR accurately is lacking. Our study aims to develop ensemble learning models using longitudinal multiparametric MRI to predict pCR in breast cancer. Methods: From July 2015 to December 2021, we collected pre-NAC and post-NAC multiparametric MRI sequences per patient. We then extracted 14,676 radiomics and 4096 deep learning features and calculated additional delta-value features. In the primary cohort (n = 409), the inter-class correlation coefficient test, U-test, Boruta and the least absolute shrinkage and selection operator regression were used to select the most significant features for each subtype of breast cancer. Five machine learning classifiers were then developed to predict pCR accurately for each subtype. The ensemble learning strategy was used to integrate the single-modality models. The diagnostic performances of models were evaluated in the three external cohorts (n = 343, 170 and 340, respectively). Findings: A total of 1262 patients with breast cancer from four centers were enrolled in this study, and pCR rates were 10.6% (52/491), 54.3% (323/595) and 37.5% (66/176) in HR+/HER2-, HER2+ and TNBC subtype, respectively. Finally, 20, 15 and 13 features were selected to construct the machine learning models in HR+/HER2-, HER2+ and TNBC subtypes, respectively. The multi-Layer Perception (MLP) yields the best diagnostic performances in all subtypes. For the three subtypes, the stacking model integrating pre-, post- and delta-models yielded the highest AUCs of 0.959, 0.974 and 0.958 in the primary cohort, and AUCs of 0.882-0.908, 0.896-0.929 and 0.837-0.901 in the external validation cohorts, respectively. The stacking model had accuracies of 85.0%-88.9%, sensitivities of 80.0%-86.3%, and specificities of 87.4%-91.5% in the external validation cohorts. Interpretation: Our study established a novel tool to predict the responses of breast cancer to NAC and achieve excellent performance. The models could help to determine post-NAC surgery strategy for breast cancer. Funding: This study is supported by grants from the National Natural Science Foundation of China (82171898, 82103093), the Deng Feng project of high-level hospital construction (DFJHBF202109), the Guangdong Basic and Applied Basic Research Foundation (grant number, 2020A1515010346, 2022A1515012277), the Science and Technology Planning Project of Guangzhou City (202002030236), the Beijing Medical Award Foundation (YXJL-2020-0941-0758), and the Beijing Science and Technology Innovation Medical Development Foundation (KC2022-ZZ-0091-5). Funding sources were not involved in the study design, data collection, analysis and interpretation, writing of the report, or decision to submit the article for publication.

Prognostic predictors of radical resection of stage I-IIIB non-small cell lung cancer: the role of preoperative CT texture features, conventional imaging features, and clinical features in a retrospectively analyzed.

BACKGROUND: To investigate the value of preoperative computed tomography (CT) texture features, routine imaging features, and clinical features in the prognosis of non-small cell lung cancer (NSCLC) after radical resection. METHODS: Demographic parameters and clinically features were analyzed in 107 patients with stage I-IIIB NSCLC, while 73 of these patients received CT scanning and radiomic characteristics for prognosis assessment. Texture analysis features include histogram, gray size area matrix and gray co-occurrence matrix features. The clinical risk features were identified using univariate and multivariate logistic analyses. By incorporating the radiomics score (Rad-score) and clinical risk features with multivariate cox regression, a combined nomogram was built. The nomogram performance was assessed by its calibration, clinical usefulness and Harrell's concordance index (C-index). The 5-year OS between the dichotomized subgroups was compared using Kaplan-Meier (KM) analysis and the log-rank test. RESULTS: Consisting of 4 selected features, the radiomics signature showed a favorable discriminative performance for prognosis, with an AUC of 0.91 (95% CI: 0.84 ~ 0.97). The nomogram, consisting of the radiomics signature, N stage, and tumor size, showed good calibration. The nomogram also exhibited prognostic ability with a C-index of 0.91 (95% CI, 0.86-0.95) for OS. The decision curve analysis indicated that the nomogram was clinically useful. According to the KM survival curves, the low-risk group had higher 5-year survival rate compared to high-risk. CONCLUSION: The as developed nomogram, combining with preoperative radiomics evidence, N stage, and tumor size, has potential to preoperatively predict the prognosis of NSCLC with a high accuracy and could assist to treatment for the NSCLC patients in the clinic.

Neoadjuvant therapy in triple-negative breast cancer: A systematic review and network meta-analysis.

BACKGROUND: Evidence for the preferred neoadjuvant therapy regimen in triple-negative breast cancer (TNBC) is not yet established. METHODS: Literature search was conducted from inception to February 12, 2022. Phase 2 and 3 randomized controlled trials (RCTs) investigating neoadjuvant therapy for TNBC were eligible. The primary outcome was pathologic complete response (pCR); the secondary outcomes were all-cause treatment discontinuation, disease-free survival or event-free survival (DFS/EFS), and overall survival. Odd ratios (OR) with 95% credible intervals (CrI) were used to estimate binary outcomes; hazard ratios (HR) with 95% CrI were used to estimate time-to-event outcomes. Bayesian network meta-analysis was implemented for each endpoint. Sensitivity analysis and network meta-regression were done. RESULTS: 41 RCTs (N = 7109 TNBC patients) were eligible. Compared with anthracycline- and taxane-based chemotherapy (ChT), PD-1 inhibitor plus platinum plus anthracycline- and taxane-based ChT was associated with a significant increased pCR rate (OR 3.95; 95% CrI 1.81-9.44) and a higher risk of premature treatment discontinuation (3.25; 1.26-8.29). Compared with dose-dense anthracycline- and taxane-based ChT, the combined treatment was not associated with significantly improved pCR (OR 2.57; 95% CrI 0.69-9.92). In terms of time-to-event outcomes, PD-1 inhibitor plus platinum plus anthracycline- and taxane-based ChT was associated with significantly improved DFS/EFS (HR 0.42; 95% CrI 0.19-0.81). CONCLUSIONS: PD-1 inhibitor plus platinum and anthracycline- and taxane-based ChT was currently the most efficacious regimen for pCR and DFS/EFS improvement in TNBC. The choice of chemotherapy backbone, optimization of patient selection with close follow-up and proactive symptomatic managements are essential to the antitumor activity of PD-1 inhibitor.

Living in a rural area: a potential risk factor for falls in people with Parkinson disease.

INTRODUCTION: Falls are prevalent in patients with Parkinson disease (PD). Previous work focused on the impact of motor and non-motor symptoms on falls and ignored the impact of environmental factors, such as residence, economic level, and nursing status. The aim of this study was to investigate the prevalence and risk factors of falls in patients with PD and explore the impact of residence on falls. METHODS: A cross-sectional study of 100 patients with PD was carried out. Patients were recruited from Anhui Provincial Hospital (Hefei, Anhui province, China) between July 2017 and December 2020. Participants were grouped based on whether they had fallen in the previous 3 months, and demographic information was collected through detailed interviews. In addition, severity of motor symptoms, cognitive function, and self-care abilities were assessed with the Unified Parkinson's Disease Rating Scale part III (UPDRS-III), the Hoehn-Yahr (H&Y) scale, the Mini-Mental State Examination (MMSE), and the Barthel Index. The results were analyzed using student t-test, Mann-Whitney U-test, χ2 test and multivariate binary logistic regression analyses. RESULTS: A total of 42% of the patients had fallen in the previous 3 months. The patients who had fallen were older and with a longer disease period, a higher UPDRS-III score, a higher H&Y stage, a lower MMSE score, and a lower Barthel Index score (all p<0.05). According to the logistic regression analysis, living in a rural area (odds ratio (OR)=3.34, 95% confidence interval (CI) 1.15-9.65), MMSE<24 (OR=4.79, 95%CI 1.17-19.65), having sleep disorders (OR=4.97, 95%CI 1.74-14.2), and having a high UPDRS-III score (OR=1.07, 95%CI 1.02-1.11) were independent risk factors for falls. The incidence of falls was higher in rural areas. Urban and rural patients showed different levels of disease severity; rural patients had higher H&Y stages, higher UPDRS-III scores and lower Barthel Index scores. CONCLUSION: Falls are caused by a variety of factors in people with PD. Multidimensional factors should be considered comprehensively to develop a personalized plan to prevent falls in PD patients.

A Semisupervised Majority Weighted Vote Antiphishing Attacks IDS for the Education Industry.

Although the digital transformation is advancing, a significant portion of the population in all countries of the world is not familiar with the technological means that allow malicious users to deceive them and gain great financial benefits using phishing techniques. Phishing is an act of deception of Internet users. The perpetrator pretends to be a credible entity, abusing the lack of protection provided by electronic tools and the ignorance of the victim (user) to illegally obtain personal information, such as bank account codes and sensitive private data. One of the most common targets for digital phishing attacks is the education sector, as distance learning became necessary for billions of students worldwide during the pandemic. Many educational institutions were forced to transition to the digital environment with minimal or no preparation. This paper presents a semisupervised majority-weighted vote system for detecting phishing attacks in a unique case study for the education sector. A realistic majority weighted vote scheme is used to optimize learning ability in selecting the most appropriate classifier, which proves to be exceptionally reliable in complex decision-making environments. In particular, the voting naive Bayes positive algorithm is presented, which offers an innovative approach to the probabilistic part-supervised learning process, which accurately predicts the class of test snapshots using prerated training snapshots only from the positive class examples.

A Privacy-Preserved Variational-Autoencoder for DGA Identification in the Education Industry and Distance Learning.

One of the most insidious methods of bypassing security mechanisms in a modern information system is the domain generation algorithms (DGAs), which are used to disguise the identity of malware by periodically switching the domain name assigned to a command and control (C&C) server. Combating advanced techniques, such as DGAs, is an ongoing challenge that security organizations often need to work with and possibly share private data to train better and more up-to-date machine learning models. This logic raises serious concerns about data integrity, trade-related issues, and strict privacy protocols that must be adhered to. To address the concerns regarding the privacy and security of private data, we propose in this work a privacy-preserved variational-autoencoder to DGA combined with case studies from the education industry and distance learning, specifically because the recent pandemic has brought an explosive increase to remote learning. This is a system that, using the secured multi-party computation (SMPC) methodology, can successfully apply machine learning techniques, specifically the Siamese variational-autoencoder algorithm, on encrypted data and metadata. The method proposed for the first time in the literature facilitates learning specialized extraction functions of useful intermediate representations in complex deep learning architectures, producing improved training stability, high generalization performance, and remarkable categorization accuracy.

The 14-3-3 protein GF14c positively regulates immunity by modulating the protein homoeostasis of the GRAS protein OsSCL7 in rice.

Various types of transcription factors have been reported to be involved in plant-pathogen interactions by regulating defence-related genes. GRAS proteins, plant- specific transcription factors, have been shown to play essential roles in plant growth, development and stress responses. By performing a transcriptome study on rice early defence responses to Magnaporthe oryzae, we identified a GRAS protein, OsSCL7, which was induced by M. oryzae infection. We characterized the function of OsSCL7 in rice disease resistance. OsSCL7 was upregulated upon exposure to M. oryzae and pathogen-associated molecular pattern treatments, and knocking out OsSCL7 resulted in decreased disease resistance of rice to M. oryzae. In contrast, overexpression of OsSCL7 could improve rice disease resistance to M. oryzae. OsSCL7 was mainly localized in the nucleus and showed transcriptional activity. OsSCL7 can interact with GF14c, a 14-3-3 protein, and loss-of-function GF14c leads to enhanced susceptibility to M. oryzae. Additionally, OsSCL7 protein levels were reduced in the gf14c mutant and knocking out OsSCL7 affected the expression of a series of defence-related genes. Taken together, these findings uncover the important roles of OsSCL7 and GF14c in plant immunity and a potential mechanism by which plants fine-tune immunity by regulating the protein stability of a GRAS protein via a 14-3-3 protein.

A Comprehensive Nomogram Combining CT Imaging with Clinical Features for Prediction of Lymph Node Metastasis in Stage I-IIIB Non-small Cell Lung Cancer.

OBJECTIVE: The status of lymph node metastasis (LNM) is highly correlated with the recurrence and survival outcomes of patients with lung cancer. Thus, a tool that predicts LNM could benefit patient treatment and prognosis. The present study established a new radiomic model by combining computed tomography (CT) radiomic features and clinical parameters to predict the LNM status in patients with non-small cell lung cancer (NSCLC). METHODS: Demographic parameters and clinical laboratory values were analyzed in 217 patients with stage I-IIIB NSCLC; 107 of the patients received CT scanning and radiomic characteristics were used for LNM assessment (76 in the training cohort and 31 in the validation cohort). The minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) regression model were used to select the most predictive features on the basis of the 76 patients in the training set. The value of the area under the receiver operator characteristic (ROC) curve (AUC) was adopted to determine the correlation between LN status and the radiomics signature in training cohorts and then validated in the 31 patients of validation set. The radiomics nomogram was analyzed using univariate and multivariate logistic regression. Decision curve analysis (DCA) was performed to evaluate the clinical utility of this model. RESULTS: This was a retrospective study. Five radiomic characteristics were significantly correlated with LNM in the two cohorts (P < 0.05). The radiomic nomogram that incorporated the above radiomic characteristics, the RDW, and the CT-based LN status had satisfactory discrimination and calibration in the training (AUC, 0.79; 95% CI 0.69-0.89) and validation cohorts (AUC, 0.70; 95% CI 0.50-0.89).The DCA showed that the developed nomogram had promising clinical utility. CONCLUSIONS: The developed nomogram, combined with preoperative radiomics evidence, the RDW, and the CT-based LN status, has the potential to preoperatively predict LNM with high accuracy and can facilitate the prediction of LN status for NSCLC patients.