Noncovalent composites of meso-substituted A2B2-porphyrins and carbon nanotubes for enhanced electrocatalytic oxygen reduction.

Exploring highly active oxygen reduction electrocatalysts with low precious metals content is imperative but remains a considerable challenge. Herein, a series of heterobimetallic multi-walled carbon nanotubes (MWCNTs) electrocatalysts based on metal complexes are presented. These electrocatalysts feature diverse transition metals (M=Mn, Fe, Co, Ni) 5,15-bromophenyl-10, 20-methoxyphenyl porphyrin (MBMP) and tetrakis(triphenylphosphine)palladium (0) (Pd[P(Ph3)4]) anchored non-covalently on its surface. The resulting NiBMP-based MWCNTs with Pd[P(Ph3)4] (PdNiN4/MWCNTs) display outstanding electrocatalytic oxygen reduction activity (onset potential, 0.941 V; half wave potential, 0.830 V) and robust long-term durability in alkaline electrolyte. While in neutral condition, the MnBMP-based MWCNTs with Pd[P(Ph3)4] (PdMnN4/MWCNTs) are the most active heterobimetallic ORR catalyst and produce ultra-low concentration hydrogen peroxide (H2O2yield, 1.2%-1.3%). Synergistically tuning the ORR electrocatalytic activity and electron transfer pathway is achieved by the formation of NiBMP/MnBMP-Pd[P(Ph3)4] active sites. This work indicates such metalloporphyrin-Pd[P(Ph3)4] active sites on MWCNTs have significantly positive influence on electrocatalytic ORR systems and provides facile and mild strategy for designing highly efficient ORR electrocatalysts with ultra-low loading precious metal.

The impact of the chalcogen-substitution element and initial spectroscopic state on excited-state relaxation pathways in nucleobase photosensitizers: a combination of static and dynamic studies.

The substitution of oxygen with chalcogen in carbonyl group(s) of canonical nucleobases gives an impressive triplet generation, enabling their promising applications in medicine and other emerging techniques. The excited-state relaxation S2(ππ*) → S1(nπ*) → T1(ππ*) has been considered the preferred path for triplet generation in these nucleobase derivatives. Here, we demonstrate enhanced quantum efficiency of direct intersystem crossing from S2 to triplet manifold upon substitution with heavier chalcogen elements. The excited-state relaxation dynamics of sulfur/selenium substituted guanines in a vacuum is investigated using a combination of static quantum chemical calculations and on-the-fly excited-state molecular dynamics simulations. We find that in sulfur-substitution the S2 state predominantly decays to the S1 state, while upon selenium-substitution the S2 state deactivation leads to simultaneous population of the S1 and T2,3 states in the same time scale and multi-state quasi-degeneracy region S2/S1/T2,3. Interestingly, the ultrafast deactivation of the spectroscopic S3 state of both studied molecules to the S1 state occurs through a successive S3 → S2 → S1 path involving a multi-state quasi-degeneracy S3/S2/S1. The populated S1 and T2 states will cross the lowest triplet state, and the S1 → T intersystem crossing happens in a multi-state quasi-degeneracy region S1/T2,3/T1 and is accelerated by selenium-substitution. The present study reveals the influence of both the chalcogen substitution element and initial spectroscopic state on the excited-state relaxation mechanism of nucleobase photosensitizers and also highlights the important role of multi-state quasi-degeneracy in mediating the complex relaxation process. These theoretical results provide additional insights into the intrinsic photophysics of nucleobase-based photosensitizers and are helpful for designing novel photo-sensitizers for real applications.

Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM).

PURPOSE: Our purposes were (1) to explore the methodologic quality of the studies on the deep learning in knee imaging with CLAIM criterion and (2) to offer our vision for the development of CLAIM to assure high-quality reports about the application of AI to medical imaging in knee joint. MATERIALS AND METHODS: A Checklist for Artificial Intelligence in Medical Imaging systematic review was conducted from January 1, 2015, to June 1, 2020, using PubMed, EMBASE, and Web of Science databases. A total of 36 articles discussing deep learning applications in knee joint imaging were identified, divided by imaging modality, and characterized by imaging task, data source, algorithm type, and outcome metrics. RESULTS: A total of 36 studies were identified and divided into: X-ray (44.44%) and MRI (55.56%). The mean CLAIM score of the 36 studies was 27.94 (standard deviation, 4.26), which was 66.53% of the ideal score of 42.00. The CLAIM items achieved an average good inter-rater agreement (ICC 0.815, 95% CI 0.660-0.902). In total, 32 studies performed internal cross-validation on the data set, while only 4 studies conducted external validation of the data set. CONCLUSIONS: The overall scientific quality of deep learning in knee imaging is insufficient; however, deep learning remains a promising technology for diagnostic or predictive purpose. Improvements in study design, validation, and open science need to be made to demonstrate the generalizability of findings and to achieve clinical applications. Widespread application, pre-trained scoring procedure, and modification of CLAIM in response to clinical needs are necessary in the future. KEY POINTS: • Limited deep learning studies were established in knee imaging with mean score of 27.94, which was 66.53% of the ideal score of 42.00, commonly due to invalidated results, retrospective study design, and absence of a clear definition of the CLAIM items in detail. • A previous trained data extraction instrument allowed reaching moderate inter-rater agreement in the application of the CLAIM, while CLAIM still needs improvement in scoring items and result reporting to become a wide adaptive tool in reviews of deep learning studies.

Automated prediction of the neoadjuvant chemotherapy response in osteosarcoma with deep learning and an MRI-based radiomics nomogram.

OBJECTIVES: To implement a pipeline to automatically segment the ROI and to use a nomogram integrating the MRI-based radiomics score and clinical variables to predict responses to neoadjuvant chemotherapy (NAC) in osteosarcoma patients. METHODS: A total of 144 osteosarcoma patients treated with NAC were separated into training (n = 101) and test (n = 43) groups. After normalisation, ROIs for the preoperative MRI were segmented by a deep learning segmentation model trained with nnU-Net by using two independent manual segmentations as labels. Radiomics features were extracted using automatically segmented ROIs. Feature selection was performed in the training dataset by five-fold cross-validation. The clinical, radiomics, and clinical-radiomics models were built using multiple machine learning methods with the same training dataset and validated with the same test dataset. The segmentation model was evaluated by the Dice coefficient. AUC and decision curve analysis (DCA) were employed to illustrate the model performance and clinical utility. RESULTS: 36/144 (25.0%) patients were pathological good responders (pGRs) to NAC, while 108/144 (75.0%) were non-pGRs. The segmentation model achieved a Dice coefficient of 0.869 on the test dataset. The clinical and radiomics models reached AUCs of 0.636 with a 95% confidence interval (CI) of 0.427-0.860 and 0.759 (95% CI, 0.589-0.937), respectively, in the test dataset. The clinical-radiomics nomogram demonstrated good discrimination, with an AUC of 0.793 (95% CI, 0.610-0.975), and accuracy of 79.1%. The DCA suggested the clinical utility of the nomogram. CONCLUSION: The automatic nomogram could be applied to aid radiologists in identifying pGRs to NAC. KEY POINTS: • The nnU-Net trained by manual labels enables the use of an automatic segmentation tool for ROI delineation of osteosarcoma. • A pipeline using automatic lesion segmentation and followed by a radiomics classifier could aid the evaluation of NAC response of osteosarcoma. • A predictive nomogram composed of clinical variables and MRI-based radiomics score provides support for individualised treatment planning.

The prevalence and parameters of fabella and its association with medial meniscal tear in China: a retrospective study of 1011 knees.

BACKGROUND: Fabella is a sesamoid bone of knee that has potential biomechanical function. We aimed to examine the fabellar prevalence and parameters in Chinese population and test the hypothesis that fabellar presence and morphology were associated with meniscus tear or ligament injury. METHODS: A total of 1011 knee magnetic resonance imaging scans from 979 patients with knee pain were analyzed retrospectively. The exclusion criteria are postsurgical scans, difficulty in fabella discrimination, conditions not suitable for measurement, and unsatisfied image. The fabellar presence and its parameters (length, width and thickness) were documented. The association between fabellar presence and meniscus tear or ligament injury were assessed by chi-square test, in all knees and subgroups (age, gender, side, lesion part). The correlation of fabellar presence and parameters with advancing age was assessed by Spearman correlation analysis. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to investigate whether factors related with meniscus tear or ligament injury. Diagnostic performance of risk factors was assessed by receiver operating characteristic (ROC) analysis. RESULTS: The overall prevalence of fabellae was 39.8% (402/1011 knees) and increased with the increasing age (r = 0.237, P < 0.001). The size of the fabellae differed according to genders, age, and presence of articulating grooves. Fabella presented more often in knees with medial meniscus (MM) tears (66.7% vs 33.8%; P < 0.001) with a multivariate OR of 2.960 (95% CI, 1.853-3.903). The association remained in all tear parts (anterior, middle, and posterior), and in younger (age < 50 years) and older patients (age ≥ 50 years). Age, fabellar length, width, length/thickness ratio and width/thickness ratio yielded an area under the ROC curve (AUC) of 0.604-0.766 to predict an MM tear. In combination with age, fabellar width and length/thickness ratio, the AUC was improved 0.791 (95% CI, 0.744-0.837), with a sensitivity of 73.0% and a specificity of 74.6%. CONCLUSION: The presence of fabellae, increased fabellar length and width as well as flatter fabellar morphology, are significantly associated with an increased risk for MM tear. These findings might aid clinicians in identifying patients at risk for a MM tear and informing them.

A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation.

OBJECTIVES: To assess the methodological quality and risk of bias in radiomics studies investigating diagnosis, therapy response, and survival of patients with osteosarcoma. METHODS: In this systematic review, literatures on radiomics in osteosarcoma were included and assessed for methodological quality through the radiomics quality score (RQS). The risk of bias and concern of application was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. A meta-analysis of studies focusing on predicting osteosarcoma response to neoadjuvant chemotherapy was performed. RESULTS: Twelve radiomics studies exploring osteosarcoma were identified, and five were included in meta-analysis. The RQS reached an average of 20.4% (6.92 of 36) with good inter-rater agreement (ICC 0.95, 95% CI 0.85-0.99). Four studies validated results with an internal dataset, none of which used external dataset; one study was prospectively designed, and another one shared part of the dataset. The risk of bias and concern of application were mainly related to index test aspect. The meta-analysis showed a diagnostic odds ratio of 43.68 (95%CI 13.5-141.31) for predicting response to neoadjuvant chemotherapy with high heterogeneity and low methodological quality. CONCLUSIONS: The overall scientific quality of included studies is insufficient; however, radiomics remains a promising technology for predicting treatment response, which might guide therapeutic decision-making and related to prognosis. Improvements in study design, validation, and open science needs to be made to demonstrate the generalizability of findings and to achieve clinical applications. Widespread application of RQS, pre-trained RQS scoring procedure, and modification of RQS in response to clinical needs are necessary. KEY POINTS: • Limited radiomics studies were established in osteosarcoma with mean RQS of 20.4%, commonly due to unvalidated results, retrospective study design, and absence of open science. • Meta-analysis of radiomics studies predicting osteosarcoma response to neoadjuvant chemotherapy showed high diagnostic odds ratio 43.68, while high heterogeneity and low methodological quality were the main concerns. • A previously trained data extraction instrument allowed reaching moderate inter-rater agreement in RQS applications, while RQS still needs improvement to become a wide adaptive tool in reviews of radiomics studies, in routine self-check before manuscript submitting and in study design.

MOF-derived Ni3S4 Encapsulated in 3D Conductive Network for High-Performance Supercapacitor.

Transition-metal sulfide is a good kind of material for supercapacitors because of the large capacity. Nevertheless, the low electroconductivity, slow reaction kinetics, and limited active centers lead to poor electrochemical properties such as long-term cycling stability. In the present work, nano nickel metal-organic framework (Ni-MOF) was constructed by using the nitrogen-rich functional group ligand 2,4,6-tris(3,5-dicarboxylphenylamino)-1,3,5-triazin and compounded with carbon nanotubes (CNTs) to prepare Ni-MOF/CNTs composite, which was used as a precursor to prepare the MOFs-derived NC/Ni-Ni3S4/CNTs composite with the Ni3S4 uniformly distributed in the three-dimensional (3D) conductive network. The rich nitrogen doping and 3D conductive network constructed by CNTs improved the conductivity, prompted the rapid entry of electrolyte, and improved the reaction kinetics of NC/Ni-Ni3S4/CNTs, thus obtained excellent specific capacitance, coulomb efficiency, and cyclic stability. The specific capacitance of NC/Ni-Ni3S4/CNTs is 1489.9 F/g at 1 A/g, which remains 800 F/g at 10 A/g, showing good rate performance.

Chondromyxoid fibroma-like osteosarcoma: a case series and literature review.

BACKGROUND: Chondromyxoid fibroma-like osteosarcoma (CMF-OS) is an exceedingly rare subtype of low-grade central osteosarcoma (LGCO), accounting for up to 10% of cases and making it difficult to diagnose. CMF-OS is frequently misdiagnosed on a radiological examination and biopsy, even after the initial operation. Its treatment is a controversial issue due to its low-grade classification and actual high-grade behavior. CASE PRESENTATION: We retrospectively reviewed the medical charts of more than 2000 osteosarcoma patients between 2008 and 2019; 11 patients with CMF-OS were identified, of which six patients were treated by our institution with complete clinical characteristics, including treatment and prognosis, radiological and pathological features were reviewed. Three males and three females with a median age of 46 (range 22-56) years were pathologically proven to have CMF-OS. The radiological presentation of CMF-OS is variable, thus radiological misdiagnoses are common. However, one must not ignore a malignant radiologic appearance. The most distinctive pathological feature conferring the diagnosis of CMF-OS is the presence of osteoid production directly by the tumor cells under a chondromyxoid fibroma (CMF)-like background. Differential diagnoses based on comprehensive data from CMF, LGCO, chondrosarcoma (CHS), conventional osteosarcoma (COS), etc., are needed. All patients were treated with an operation and chemotherapy, and one patient received additional radiotherapy. Nevertheless, recurrence and metastasis are common in CMF-OS patients. Relatively invasive biological behavior of CMF-OS is against the low-grade classification of this disease. CONCLUSIONS: It is important to recognize CMF-OS and distinguish it from CMF, CHS, COS and other LGCOs. CMF-OS has a relatively poor prognosis despite its low-grade classification.

Synthesis, characterization, and photodynamic therapy activity of 5,10,15,20-Tetrakis(carboxyl)porphyrin.

Water-soluble porphyrins are considered promising drug candidates for photodynamic therapy (PDT). This study investigated the PDT activity of a new water-soluble, anionic porphyrin (1-Zn), which possesses four negative charges. The photodynamic anticancer activity of 1-Zn was investigated by the MTT assay, with mTHPC as a positive control. The cellular distribution was determined by fluorescence microscopy. Holographic and phase contrast images were recorded after 1-Zn treatment with a HoloMonitor™ M3 instrument. The inhibition of A549 cell growth achieved by inducing apoptosis was investigated by flow cytometry and fluorescence microscopy. DNA damage was investigated by the comet assay. The expression of apoptosis-related proteins was also measured by western blot assays. 1-Zn had better phototoxicity against A549 cells than HeLa and HepG2 cancer cells. Interestingly, 1-Zn was clearly located almost entirely in the cell cytoplasmic region/organelles. The late apoptotic population was less than 1.0% at baseline in the untreated and only light-treated cells and increased to 40.5% after 1-Zn treatment and irradiation (P < 0.05). 1-Zn triggered significant ROS generation after irradiation, causing ΔΨm disruption (P < 0.01) and DNA damage. 1-Zn induced A549 cell apoptosis via the mitochondrial apoptosis pathway. In addition, 1-Zn bound in the groove of DNA via an outside binding mode by pi-pi stacking and hydrogen bonding. 1-Zn exhibits good photonuclease activity and might serve as a potential photosensitizer (PS) for lung cancer cells.

Synergistic effect of a cobalt fluoroporphyrin and graphene oxide on the simultaneous voltammetric determination of catechol and hydroquinone.

Graphene oxide (GO) was modified with the cobalt(II) and zinc(II) complexes (CoTFPP and ZnTFPP) of 5,10,15,20-tetrakis(pentafluorophenyl)porphyrin in order to improve the electrocatalytic activity of GO towards catechol (CC) and hydroquinone (HQ). It is found that the CoTFPP-modified GO on a glassy carbon electrode (GCE) displays the highest electrocatalytic activity. The response to CC (at 0.14 V vs. SCE) is linear in the 1-220 µM concentration range. The response to HQ (at 0.04 V vs. SCE) extends from 1 µM to 200 µM. The sensitivity and detection limits are 10.40 µA∙µM-1∙cm-2 and 0.17 µM for CC, and 8.40 µA∙µM-1∙cm-2 and 0.21 µM for HQ. Experimental results indicate that the Co(II) and Zn(II) ions in the porphyrins positively affect the electron transfer rate in the hybrid materials. The GCE modified with CoTFPP/GO was successfully applied to the simultaneous determination of CC and HQ in spiked samples of tap and lake water. Graphical abstract Schematic presentation of a voltammetric method for simultaneous determination of catechol (CC) and hydroquinone (HQ). It is based on the use of a cobalt (II) fluoroporphyrin (CoTFPP) functionalized graphene oxide (GO) hybrid.

Correction to: Synergistic effect of a cobalt fluoroporphyrin and graphene oxide on the simultaneous voltammetric determination of catechol and hydroquinone.

The published version of this article, unfortunately, contains error. The author found out that Chart 1 image was wrongly incorporated in the online paper.

Relationship between subchondral bone microstructure and articular cartilage in the osteoarthritic knee using 3T MRI.

BACKGROUND: The importance of subchondral bone in the pathogenesis of osteoarthritis (OA) has drawn interest. PURPOSE: To investigate subregional trabecular bone microstructural features and to determine the relationship between cartilage and trabecular bone in the osteoarthritic human knee. STUDY TYPE: Prospective study. SUBJECTS: In all, 92 knees were enrolled and divided into three groups: without OA, mild OA, and severe OA. SEQUENCE: A sagittal 3D balanced fast field echo (3D B-FFE) sequence and FatSat 3D fast field echo (3D-FFE) sequence at 3T MRI. ASSESSMENT: The trabecular bone in 12 sites of the knee joint was evaluated using digital topological analysis, and the cartilage thickness in four sites was calculated. STATISTICAL TEST: Trabecular bone and cartilage parameters between groups were compared using analysis of variance (ANOVA) with Bonferroni adjustment, and their correlations were analyzed using Pearson's correlation coefficient. RESULTS: Within both femoral condyles, the trabecular bone structure deteriorated in mild OA, showing a lower bone volume fraction (BVF) (0.15 to 0.12, P < 0.05), higher erosion index (EI) (2.25 to 2.28, P < 0.01), and a lower plate-to-rod ratio (SCR) (6.22 to 5.96, P < 0.05). Within medial and lateral tibia, deterioration in the trabecular bone was also observed, demonstrating a lower BVF (0.15 to 0.12 P < 0.05) and a higher EI (2.25 to 2.61, P < 0.05). Cartilage attrition mainly occurred in the medial joint. Extensive correlations were found between the medial cartilage thickness and subregional trabecular parameters. There was mainly a positive correlation with both femoral BVFs (r > 0.3, P < 0.05), and a negative correlation with the tibia SCR (r < -0.2, P < 0.05). DATA CONCLUSION: In the early stage of OA, trabecular bone osteoporotic changes were observed in both femoral condyles and the tibia. Based on a strong correlation with the medial cartilage thickness, the trabecular structural topological analysis may be useful for elucidating OA onset and progression. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Quantitative evaluation of subchondral bone microarchitecture in knee osteoarthritis using 3T MRI.

BACKGROUND: Osteoarthritis (OA) is now increasingly recognized as being related to the whole joint instead of the cartilage alone. In particular, the importance of subchondral bone in OA pathogenesis has drawn a lot of interest. The aim of this study is to investigate subchondral bone microstructural features in two femoral condyles of human knee osteoarthritis. METHODS: Eighty subjects were enrolled in our study and divided into three groups: without OA (group 0), mild OA (group 1), and severe OA (group 2). Sagittal 3D Balanced Fast Field Echo (3D-FFE) images were obtained by 3T MRI to quantify trabecular bone structure, and sagittal FatSat 3D Fast Field Echo (3D-FFE) images were acquired to assess cartilage thickness. Trabecular bone parameters, including bone volume fraction (BVF), erosion index (EI) and the trabecular plate-to-rod ratio (SCR), and trabecular thickness were evaluated using digital topological analysis. Subchondral bone and cartilage parameters between different groups and different locations were compared, and their correlations were analyzed. RESULTS: Within two femoral condyles, subchondral bone structure was deteriorated in mild OA, showing a lower BVF (-0.011 to -0.014 P < 0.001), a higher EI (0.346 to 0.310 P < 0.001), a lower SCR (-0.581 to -0.542 P < 0.001)) and lower trabecular thickness (-6.588 to -4.759 P < 0.05). In severe OA, BVF was further decreased, but EI, SCR and trabecular thickness showed no significant difference than mild OA(P > 0.05). Moreover, there was a lower BVF, SCR and higher EI in the medial femoral condyle in each group. Interestingly, cartilage attrition mainly occurred in the medial femoral condyle. Medial cartilage thickness was not only positively correlated with the ipsilateral femoral BVF (r = 0.321 P = 0.004) but also with the opposite femoral BVF (r = 0.270 P = 0.015). CONCLUSIONS: Our results indicated that deterioration in the trabecular bone structure in both femoral condyles could more sensitively reveal early OA, and BVF could be a better biomarker to evaluate OA severity.

Porphyrin dyes on TiO2 surfaces with different orientations: a photophysical, photovoltaic, and theoretical investigation.

Porphyrin dyes with a triphenylamino group as an electron donor, para- or meta-benzoic acids as electron acceptors, and hydrogen (H) or mesityl (M) substituents on the meso position as auxiliary groups were synthesized. Their photophysical properties and photovoltaic performance in dye-sensitized solar cells were investigated. All four porphyrins exhibited similar photophysical properties in the solution and dye-loading densities on the surface of TiO2 nanoparticles; however, the p-benzoic acid functionalized porphyrins, p-H(M)PZn, gave better photovoltaic performance than m-benzoic acid functionalized porphyrins, m-H(M)PZn. Theoretical calculations indicated that the electron density on the frontier molecular orbital was more delocalized to p-benzoic acid than to m-benzoic acid. Absorption spectra indicated the stronger H-aggregation in m-H(M)PZn than that in p-H(M)PZn on the surface of TiO2 nanoparticles. The mesityl groups in the meso positions reduced the dye-loading density due to steric hindrance between dyes. As a result, the p-MPZn exhibited the best energy conversion efficiency among the four porphyrins studied. This efficiency was further enhanced when a complementary dye BET was used.

2D Metal Porphyrin-Based MOFs and ZIF-8 Composite-Derived Carbon Materials Containing M-Nx Active Sites as Bifunctional Electrocatalysts for Zinc-Air Batteries.

The main impediment to the development of zinc-air batteries is the sluggish kinetics of the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER). Transition metal N-doped carbon catalysts offer a promising alternative to noble metal catalysts, with metal-organic framework (MOF)-derived carbon material catalysts being particularly noteworthy. Here, we synthesized MxP-Z-C carbon catalysts by combining two-dimensional (2D) metal porphyrin-based MOFs (MxPMFs, x = Fe, Co, Ni, Mn) and three-dimensional zeolitic imidazole framework-8 (ZIF-8) through electrostatic interaction, followed by carbonization. ZIF-8 was inserted between the layers of MxPMFs to prevent its Π-Π stacking, allowing the active sites to become fully exposed. MxP-Z-C demonstrated an impressive catalytic activity for both the ORR and the OER reactions. Among them, FeP-Z-C showed the best catalytic activity. The half-wave potential for ORR was 0.92 V (vs the reversible hydrogen electrode (RHE)), while the overpotential for the OER was 290 mV. In addition, the zinc-air battery assembled by FeP-Z-C exhibited high power density (133.14 mW cm-2) and significant specific capacity (816 mAh gZn-1), indicating considerable potential as a bifunctional catalyst for electronic devices.

Sequential model for predicting patient adherence in subcutaneous immunotherapy for allergic rhinitis.

Objective: Subcutaneous Immunotherapy (SCIT) is the long-lasting causal treatment of allergic rhinitis (AR). How to enhance the adherence of patients to maximize the benefit of allergen immunotherapy (AIT) plays a crucial role in the management of AIT. This study aims to leverage novel machine learning models to precisely predict the risk of non-adherence of AR patients and related local symptom scores in 3 years SCIT. Methods: The research develops and analyzes two models, sequential latent-variable model (SLVM) of Stochastic Latent Actor-Critic (SLAC) and Long Short-Term Memory (LSTM). SLVM is a probabilistic model that captures the dynamics of patient adherence, while LSTM is a type of recurrent neural network designed to handle time-series data by maintaining long-term dependencies. These models were evaluated based on scoring and adherence prediction capabilities. Results: Excluding the biased samples at the first time step, the predictive adherence accuracy of the SLAC models is from 60% to 72%, and for LSTM models, it is 66%-84%, varying according to the time steps. The range of Root Mean Square Error (RMSE) for SLAC models is between 0.93 and 2.22, while for LSTM models it is between 1.09 and 1.77. Notably, these RMSEs are significantly lower than the random prediction error of 4.55. Conclusion: We creatively apply sequential models in the long-term management of SCIT with promising accuracy in the prediction of SCIT nonadherence in AR patients. While LSTM outperforms SLAC in adherence prediction, SLAC excels in score prediction for patients undergoing SCIT for AR. The state-action-based SLAC adds flexibility, presenting a novel and effective approach for managing long-term AIT.

Multi-omics approaches to understand pathogenicity during potato early blight disease caused by Alternaria solani.

Potato early blight (PEB), a foliar disease of potato during the growing period, caused by Alternaria sp., is common in major potato-producing areas worldwide. Effective agents to control this disease or completely resistant potato varieties are absent. Large-scale use of fungicides is limited due to possibility of increase in pathogen resistance and the requirements of ecological agriculture. In this study, we focused on the composition and infection characteristics of early blight pathogens in Yunnan Province and screened candidate pathogenesis-related pathways and genes. We isolated 85 strains of Alternaria sp. fungi from typical early blight spots in three potato-growing regions in Yunnan Province from 2018 to 2022, and identified 35 strains of Alternaria solani and 50 strains of Alternaria alternata by morphological characterization and ITS sequence comparison, which were identified as the main and conditional pathogens causing early blight in potato, respectively. Scanning electron microscope analysis confirmed only A. solani producing appressorium at 4 h after inoculation successfully infected the leaf cells. Via genome assembly and annotation, combine transcriptome and proteomic analysis, the following pathogenicity-related unit, transcription factors and metabolic pathway were identified: (1) cell wall-degrading enzymes, such as pectinase, keratinase, and cellulase; (2) genes and pathways related to conidia germination and pathogenicity, such as ubiquitination and peroxisomes; and (3) transcription factors, such as Zn-clus, C2H2, bZIP, and bHLH. These elements were responsible for PEB epidemic in Yunnan.

Retraction: Radical Covalent Organic Frameworks Associated with Liquid Na-K toward Dendrite-Free Alkali Metal Anodes.

10.1002/advs.202203058 Adv. Sci. 2022, 9, 2203058 The above article from Advanced Science, published online on 21 July 2022 in Wiley Online Library (https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202203058), has been retracted by agreement between the authors, the Editor-in-Chief, Kirsten Severing, and Wiley-VCH GmbH. The retraction has been agreed as the article is based on research results and data that the authors were not authorized to use. Moreover, the majority of co-authors have been listed despite insufficient qualification for contributorship.

Retraction: Dual-Ion Stabilized Layered Structure of O-V-OforZero-Strain Potassium Insertion and Extraction.

10.1002/advs.202202550 Adv. Sci.2022, 9, 2202550 The above article from Advanced Science, published online on 5 June 2022 in Wiley Online Library (https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202202550), has been retracted by agreement between the authors, the Editor-in-Chief, Kirsten Severing, and Wiley-VCH GmbH. The retraction has been agreed as the article is based on research results and data that the authors were not authorized to use. Moreover, the majority of co-authors have been listed despite insufficient qualification for contributorship.

Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution.

Knee osteoarthritis (OA) is the most common osteoarthritis and a leading cause of disability. Cartilage defects are regarded as major manifestations of knee OA, which are visible by magnetic resonance imaging (MRI). Thus early detection and assessment for knee cartilage defects are important for protecting patients from knee OA. In this way, many attempts have been made on knee cartilage defect assessment by applying convolutional neural networks (CNNs) to knee MRI. However, the physiologic characteristics of the cartilage may hinder such efforts: the cartilage is a thin curved layer, implying that only a small portion of voxels in knee MRI can contribute to the cartilage defect assessment; heterogeneous scanning protocols further challenge the feasibility of the CNNs in clinical practice; the CNN-based knee cartilage evaluation results lack interpretability. To address these challenges, we model the cartilages structure and appearance from knee MRI into a graph representation, which is capable of handling highly diverse clinical data. Then, guided by the cartilage graph representation, we design a non-Euclidean deep learning network with the self-attention mechanism, to extract cartilage features in the local and global, and to derive the final assessment with a visualized result. Our comprehensive experiments show that the proposed method yields superior performance in knee cartilage defect assessment, plus its convenient 3D visualization for interpretability.