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Highlights Evaporation and solution two-step hybrid process are used to fabricate conformal growth of perovskite film on textured silicon substrates. F-PMAI additive enlarged the perovskite grains, induced the (111) preferred orientation and passivated interface defects. We obtained a champion efficiency of 30.05% for a monolithic perovskite/silicon tandem solar cell based on a silicon thin film tunneling junction.

Abstract Background This study aimed to evaluate the T2W hypointense ring and T2-FLAIR mismatch signs in gliomas and use these signs to construct prediction models for glioma grading and isocitrate dehydrogenase (IDH) mutation status. Methods Two independent radiologists retrospectively evaluated 207 glioma patients to assess the presence of T2W hypointense ring and T2-FLAIR mismatch signs. The inter-rater reliability was calculated using the Cohen’s kappa statistic. Two logistic regression models were constructed to differentiate glioma grade and predict IDH genotype noninvasively, respectively. Receiver operating characteristic (ROC) analysis was used to evaluate the developed models. Results Of the 207 patients enrolled (119 males and 88 females, mean age 51.6 ± 14.8 years), 45 cases were low-grade gliomas (LGGs), 162 were high-grade gliomas (HGGs), 55 patients had IDH mutations, and 116 were IDH wild-type. The number of T2W hypointense ring signs was higher in HGGs compared to LGGs (p

BackgroundThe primary imaging markers for idiopathic Normal Pressure Hydrocephalus (iNPH) emphasize morphological measurements within the ventricular system, with no attention given to alterations in brain parenchyma. This study aimed to investigate the potential effectiveness of combining ventricular morphometry and cortical structural measurements as diagnostic biomarkers for iNPH.MethodsA total of 57 iNPH patients and 55 age-matched healthy controls (HC) were recruited in this study. Firstly, manual measurements of ventricular morphology, including Evans Index (EI), z-Evans Index (z-EI), Cella Media Width (CMW), Callosal Angle (CA), and Callosal Height (CH), were conducted based on MRI scans. Cortical thickness measurements were obtained, and statistical analyses were performed using surface-based morphometric analysis. Secondly, three distinct models were developed using machine learning algorithms, each based on a different input feature: a ventricular morphology model (LVM), a cortical thickness model (CT), and a fusion model (All) incorporating both features. Model performances were assessed using 10-fold cross validation and tested on an independent dataset. Model interpretation utilized Shapley Additive Interpretation (SHAP), providing a visualization of the contribution of each variable in the predictive model. Finally, Spearman correlation coefficients were calculated to evaluate the relationship between imaging biomarkers and clinical symptoms.ResultsiNPH patients exhibited notable differences in cortical thickness compared to HC. This included reduced thickness in the frontal, temporal, and cingulate cortices, along with increased thickness in the supracentral gyrus. The diagnostic performance of the fusion model (All) for iNPH surpassed that of the single-feature models, achieving an average accuracy of 90.43%, sensitivity of 90.00%, specificity of 90.91%, and Matthews correlation coefficient (MCC) of 81.03%. This improvement in accuracy (6.09%), sensitivity (11.67%), and MCC (11.25%) compared to the LVM strategy was significant. Shap analysis revealed the crucial role of cortical thickness in the right isthmus cingulate cortex, emerging as the most influential factor in distinguishing iNPH from HC. Additionally, significant correlations were observed between the typical triad symptoms of iNPH patients and cortical structural alterations.ConclusionThis study emphasizes the significant role of cortical structure changes in the diagnosis of iNPH, providing a novel insights for assisting clinicians in improving the identification and detection of iNPH.

Capacity planning is a very important global challenge in the face of Covid-19 pandemic. In order to hedge against the fluctuations in the random demand and to take advantage of risk pooling effect, one needs to have a good understanding of the variabilities in the demand of resources. However, Covid-19 predictive models that are widely used in capacity planning typically often predict the mean values of the demands (often through the predictions of the mean values of the confirmed cases and deaths) in both the temporal and spatial dimensions. They seldom provide trustworthy prediction or estimation of demand variabilities, and therefore, are insufficient for proper capacity planning. Motivated by the literature on variability scaling in the areas of physics and biology, we discovered that in the Covid-19 pandemic, both the confirmed cases and deaths exhibit a common variability scaling law between the average of the demand μ and its standard deviation σ, that is, σ∝μβ, where the scaling parameter β is typically in the range of 0.65 to 1, and the scaling law exists in both the temporal and spatial dimensions. Based on the mechanism of contagious diseases, we further build a stylized network model to explain the variability scaling phenomena. We finally provide simple models that may be used for capacity planning in both temporal and spatial dimensions, with only the predicted mean demand values from typical Covid-19 predictive models and the standard deviations of the demands derived from the variability scaling law.

Purpose- As the health of Chinese college students continues to decline, people realize that the quality of college sport class quality is one of the main factors affecting student health. This research is through ODI to change the teaching strategy, and improve the sports motivation, sports ability and Satisfaction with teachers. It will verify the effectiveness and feasibility of ODI methods in China's sport class. Design/Methodology/Approach- Use a dual experimental group design, combination of qualitative and quantitative action research. Collect qualitative and quantitative data before and after ODI, Pairedsample t-test, Pearson correlation, and regression analysis were used to determine. This organization is a public university in the south of China. sports teachers (n=3), college students (n=127). Findings- The implementation of ODI can significantly improve sports class quality; There is a significant causal relationship between the independent variables of ecological paradigm teaching strategy (Leadership, Managerial, Social, Vector, Instructional) and the dependent variables of Sports class quality; the dependent variables (Sports motivation, Sports ability, Satisfaction with teachers) are highly correlated, Intervention of one factor can predict another factor. Practical Implications- The research will have a positive impact on the health of Shaoguan University students; The results will be used as reference for the improvement of physical education teaching in Chinese universities. Originality/Value- The study verifies the feasibility of organizational development intervention methods in Chinese sport classes; Put forward the "Ecological Paradigm Teaching Strategy Model"; Summarizes the theoretical framework, conceptual framework and action basis; it is an innovative research based on the combination of organizational development and sports class. [ABSTRACT FROM AUTHOR]

Abstract Background and purpose Ki-67 labeling index (LI) is an important indicator of tumor cell proliferation in glioma, which can only be obtained by postoperative biopsy at present. This study aimed to explore the correlation between Ki-67 LI and apparent diffusion coefficient (ADC) parameters and to predict the level of Ki-67 LI noninvasively before surgery by multiple MRI characteristics. Methods Preoperative MRI data of 166 patients with pathologically confirmed glioma in our hospital from 2016 to 2020 were retrospectively analyzed. The cut-off point of Ki-67 LI for glioma grading was defined. The differences in MRI characteristics were compared between the low and high Ki-67 LI groups. The receiver operating characteristic (ROC) curve was used to estimate the accuracy of each ADC parameter in predicting the Ki-67 level, and finally a multivariate logistic regression model was constructed based on the results of ROC analysis. Results ADCmin, ADCmean, rADCmin, rADCmean and Ki-67 LI showed a negative correlation (r = − 0.478, r = − 0.369, r = − 0.488, r = − 0.388, all P

Abstract Background To evaluate the performances of multiparametric MRI‐based convolutional neural networks (CNNs) for the preoperative assessment of breast cancer molecular subtypes. Methods A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast‐enhanced T1‐weighted imaging (T1C), Apparent diffusion coefficient (ADC), and T2‐weighted imaging (T2W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance. Results For the separation of each subtype from other subtypes on the testing set, the T1C‐based models yielded AUCs from 0.762 to 0.920; the ADC‐based models yielded AUCs from 0.686 to 0.851; and the T2W‐based models achieved AUCs from 0.639 to 0.697. Conclusion T1C‐based models performed better than ADC‐based models and T2W‐based models in assessing the breast cancer molecular subtypes. The discriminating performances of our CNN models for triple negative and human epidermal growth factor receptor 2‐enriched subtypes were better than that of luminal A and luminal B subtypes.

Abstract The fibroblast is one of the ideal target cell candidates for cell-based gene therapy approaches to promote tissue repair. Gene delivery to fibroblasts by viral transfection has been confirmed to have high transfection efficiency. However, in addition to immunogenic effects of viruses, the random integration of viral genes may damage the genome, affect the cell phenotype or even cause cancerous mutations in the transfected cells. Due to these potential biohazards and unknown long-term risks, the clinical use of viral transfection has been very limited. In contrast, initial non-viral transfection methods have been simple and safe to implement, with low immunogenicity, insertional mutagenesis, and risk of carcinogenesis, but their transfection efficiency has been relatively low. Nucleofection, a more recent non-viral transfection method, now combines the advantages of high transfection efficiency and direct nucleic acid delivery to the nucleus with a high safety. Here, we reviewed recent articles on fibroblast nucleofection, summarized different research points, improved methods and application scopes, and opened up ideas for promoting the further improvement and development of fibroblast nucleofection to meet the needs of a variety of disease research and clinical applications.

ObjectivesThe abnormal functional connectivity (FC) pattern of default mode network (DMN) may be key markers for early identification of various cognitive disorders. However, the whole-brain FC changes of DMN in delayed neurocognitive recovery (DNR) are still unclear. Our study was aimed at exploring the whole-brain FC patterns of all regions in DMN and the potential features as biomarkers for the prediction of DNR using machine-learning algorithms.MethodsResting-state functional magnetic resonance imaging (fMRI) was conducted before surgery on 74 patients undergoing non-cardiac surgery. Seed-based whole-brain FC with 18 core regions located in the DMN was performed, and FC features that were statistically different between the DNR and non-DNR patients after false discovery correction were extracted. Afterward, based on the extracted FC features, machine-learning algorithms such as support vector machine, logistic regression, decision tree, and random forest were established to recognize DNR. The machine learning experiment procedure mainly included three following steps: feature standardization, parameter adjustment, and performance comparison. Finally, independent testing was conducted to validate the established prediction model. The algorithm performance was evaluated by a permutation test.ResultsWe found significantly decreased DMN connectivity with the brain regions involved in visual processing in DNR patients than in non-DNR patients. The best result was obtained from the random forest algorithm based on the 20 decision trees (estimators). The random forest model achieved the accuracy, sensitivity, and specificity of 84.0, 63.1, and 89.5%, respectively. The area under the receiver operating characteristic curve of the classifier reached 86.4%. The feature that contributed the most to the random forest model was the FC between the left retrosplenial cortex/posterior cingulate cortex and left precuneus.ConclusionThe decreased FC of DMN with regions involved in visual processing might be effective markers for the prediction of DNR and could provide new insights into the neural mechanisms of DNR.Clinical Trial Registration: Chinese Clinical Trial Registry, ChiCTR-DCD-15006096.

Abstract Objective To investigate the morphological changes with age in the pancreases of healthy individuals undergoing magnetic resonance imaging (MRI). Methods The participants were selected from adults who were undergoing physical examinations from January 2017 to September 2020 at Huadong Hospital. They were divided according to age, as broken down by decades into seven groups ranging from 20 to 29 years to ≥80 years of age. There were 30 to 35 cases for each decade. They were then divided into a young and middle‐aged group (

The demand for larger passenger capacity and more entertainment facilities has led to the rapid growth of the cruise tourism market. The superstructure of cruise ships is designed to be plumper, with numerous decks and complex structural forms. To control the weight and the center of gravity, the bending stiffness of the superstructure is always designed to be weaker than that of the main hull, resulting in a stiffness step. Currently, there is no satisfactory method to accurately estimate the influence of the stiffness step between the main hull and superstructure on the structural response of cruise ships. In the present research, an experimental analysis is conducted to investigate the stiffness step between the main hull and superstructure of a typical cruise ship. By comparing the longitudinal stress distribution characteristics with and without the stiffness step with the theoretical results, the influence of the stiffness step on the longitudinal strength is investigated. Furthermore, the maximum stress and the bending efficiency of the superstructure are also discussed. The present research is of reference significance for the structural safety and reliability design of cruise ships.

Abstract Purpose To assess the value of the multimodel magnetic resonance imaging (MRI), including unenhanced images, dynamic contrast-enhanced MRI (DCE-MRI), MR-cholangiopancreatography (MRCP), and diffusion-weighted imaging (DWI), in differentiation of mass-forming autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC). Methods Twelve patients with mass-forming AIP and 30 with PDAC were included. All patients underwent unenhanced MRI, DCE-MRI, DWI, and MRCP. Relevant values including sensitivity and specificity of the imaging features and their diagnostic performance for predicting mass-forming AIP were analyzed. Results Several statistically significant MR findings and quantitative indexes differentiating mass-forming AIP from PDAC, including multiplicity, irregularity or conformation, capsule-like rim enhancement, absence of internal cystic or necrotic portion, homogeneous enhancement during pancreatic, venous, and delayed phases, skipped stricture or stricture of MPD, absence of side branch dilation, maximum upstream MPD diameter 0.739, ContrastAP > 0.710, ContrastPP > 0.879, and ContrastVP or ContrastDP > 0.949, indicated mass-forming AIP (P

Background and PurposeGliomas are one of the most common tumors in the central nervous system. This study aimed to explore the correlation between MRI morphological characteristics, apparent diffusion coefficient (ADC) parameters and pathological grades, as well as IDH gene phenotypes of gliomas.MethodsPreoperative MRI data from 166 glioma patients with pathological confirmation were retrospectively analyzed to compare the differences of MRI characteristics and ADC parameters between the low-grade and high-grade gliomas (LGGs vs. HGGs), IDH mutant and wild-type gliomas (IDHmut vs. IDHwt). Multivariate models were constructed to predict the pathological grades and IDH gene phenotypes of gliomas and the performance was assessed by the receiver operating characteristic (ROC) analysis.ResultsTwo multivariable logistic regression models were developed by incorporating age, ADC parameters, and MRI morphological characteristics to predict pathological grades, and IDH gene phenotypes of gliomas, respectively. The Noninvasive Grading Model classified tumor grades with areas under the ROC curve (AUROC) of 0.934 (95% CI=0.895-0.973), sensitivity of 91.2%, and specificity of 78.6%. The Noninvasive IDH Genotyping Model differentiated IDH types with an AUROC of 0.857 (95% CI=0.787-0.926), sensitivity of 88.2%, and specificity of 63.8%.ConclusionMRI features were correlated with glioma grades and IDH mutation status. Multivariable logistic regression models combined with MRI morphological characteristics and ADC parameters may provide a noninvasive and preoperative approach to predict glioma grades and IDH mutation status.

ObjectiveThis study aimed to investigate the feasibility of preoperative intravoxel incoherent motion (IVIM) MRI for the screening of high-risk patients with moyamoya disease (MMD) who may develop postoperative cerebral hyperperfusion syndrome (CHS).MethodsThis study composed of two parts. In the first part 24 MMD patients and 24 control volunteers were enrolled. IVIM-MRI was performed. The relative pseudo-diffusion coefficient, perfusion fraction, apparent diffusion coefficient, and diffusion coefficient (rD*, rf, rADC, and rD) values of the IVIM sequence were compared according to hemispheres between MMD patient and healthy control groups. In the second part, 98 adult patients (124 operated hemispheres) with MMD who underwent surgery were included. Preoperative IVIM-MRI was performed. The rD*, rf, rADC, rD, and rfD* values of the IVIM sequence were calculated and analyzed. Operated hemispheres were divided into CHS and non-CHS groups. Patients’ age, sex, Matsushima type, Suzuki stage, and IVIM-MRI examination results were compared between CHS and non-CHS groups.ResultsOnly the rf value was significantly higher in the healthy control group than in the MMD group (P < 0.05). Out of 124 operated hemispheres, 27 were assigned to the CHS group. Patients with clinical presentation of Matsushima types I–V were more likely to develop CHS after surgery (P < 0.05). The rf values of the ipsilateral hemisphere were significantly higher in the CHS group than in the non-CHS group (P < 0.05). The rfD* values of the ACA and MCA supply areas of the ipsilateral hemisphere were significantly higher in the CHS group than in the non-CHS group (P < 0.05). Only the rf value of the anterior cerebral artery supply area in the contralateral hemisphere was higher in the CHS group than in the non-CHS group (P < 0.05). The rf values of the middle and posterior cerebral artery supply areas and the rD, rD*, and rADC values of the both hemispheres were not significantly different between the CHS and non-CHS groups (P > 0.05).ConclusionPreoperative non-invasive IVIM-MRI analysis, particularly the f-value of the ipsilateral hemisphere, may be helpful in predicting CHS in adult patients with MMD after surgery. MMD patients with ischemic onset symptoms are more likely to develop CHS after surgery.

This article presents a consistency control algorithm for the autonomous underwater vehicle (AUV) group combined with the leader–follower approach under communication delay. First, the six-degree-of-freedom (DoF) model of AUV is represented, and the graph theory is used to describe the communication topology of the AUV group. Especially, a hybrid communication topology is introduced to adapt to large formation control. Second, the distributed control law is constructed by combining the consensus theory with the leader–follower method. The consistency control algorithms for homogeneous and heterogeneous AUV groups based on the leader–follower approach under communication delay are proposed. Stability criteria are established to guarantee the consensus based on the Gershgorin disk theorem and Nyquist law, respectively. Finally, numerous simulation experiments are carried out to show the effectiveness and superiority of the proposed algorithms.

ObjectiveTo explore the feasibility of 2D phase-contrast MRI (PC-MRI) and intravoxel incoherent motion (IVIM) MRI to assess cerebrovascular hemodynamic changes after surgery in adult patients with moyamoya disease (MMD).MethodsIn total, 33 patients with MMD who underwent 2D PC-MRI and IVIM examinations before and after surgery were enrolled. Postsurgical changes in peak and average velocities, average flow, forward volume, and the area of superficial temporal (STA), internal carotid (ICA), external carotid (ECA), and vertebral (VA) arteries were evaluated. The microvascular perfusion status was compared between the hemorrhage and non-hemorrhage groups.ResultsThe peak velocity, average flow, forward volume, area of both the ipsilateral STA and ECA, and average velocity of the ipsilateral STA were increased (p < 0.05). The average flow and forward volume of both the ipsilateral ICA and VA and the area of the ipsilateral VA were increased (p < 0.05). The peak velocity, average velocity, average flow and forward volume of the contralateral STA, and the area of the contralateral ICA and ECA were also increased (p < 0.05), whereas the area of the contralateral VA was decreased (p < 0.05). The rf value of the ipsilateral anterior cerebral artery (ACA) supply area was increased (p < 0.05) and more obvious in the non-hemorrhage group (p < 0.05).ConclusionTwo-dimensional PC-MRI and IVIM may have the potential to non-invasively evaluate cerebrovascular hemodynamic changes after surgery in patients with MMD. An improvement in the microvascular perfusion status is more obvious in patients with ischemic MMD than in patients with hemorrhagic MMD.

This study investigated the relationship between preoperative cerebral blood flow (CBF) in patients with idiopathic normal pressure hydrocephalus (INPH) and preoperative clinical symptoms and changes of clinical symptoms after shunt surgery. A total of 32 patients with diagnosed INPH and 18 age-matched healthy controls (HCs) were involved in this study. All subjects underwent magnetic resonance imaging (MRI), including 3D pulsed arterial-spin labeling (PASL) for non-invasive perfusion imaging, and clinical symptom evaluation at baseline, and all patients with INPH were reexamined with clinical tests 1 month postoperatively. Patients with INPH had significantly lower whole-brain CBF than HCs, with the most significant differences in the high convexity, temporal lobe, precuneus, and thalamus. At baseline, there was a significant correlation between the CBF in the middle frontal gyrus, calcarine, inferior and middle temporal gyrus, thalamus, and posterior cingulate gyrus and poor gait manifestation. After shunting, improvements were negatively correlated with preoperative perfusion in the inferior parietal gyrus, inferior occipital gyrus, and middle temporal gyrus. Preoperative CBF in the middle frontal gyrus was positively correlated with the severity of preoperative cognitive impairment and negatively correlated with the change of postoperative MMSE score. There was a moderate positive correlation between anterior cingulate hypoperfusion and improved postoperative urination. Our study revealed that widely distributed and intercorrelated cortical and subcortical pathways are involved in the development of INPH symptoms, and preoperative CBF may be correlative to short-term shunt outcomes.

Background and PurposeBreast ductal carcinoma in situ (DCIS) has no metastatic potential, and has better clinical outcomes compared with invasive breast cancer (IBC). Convolutional neural networks (CNNs) can adaptively extract features and may achieve higher efficiency in apparent diffusion coefficient (ADC)-based tumor invasion assessment. This study aimed to determine the feasibility of constructing an ADC-based CNN model to discriminate DCIS from IBC.MethodsThe study retrospectively enrolled 700 patients with primary breast cancer between March 2006 and June 2019 from our hospital, and randomly selected 560 patients as the training and validation sets (ratio of 3 to 1), and 140 patients as the internal test set. An independent external test set of 102 patients during July 2019 and May 2021 from a different scanner of our hospital was selected as the primary cohort using the same criteria. In each set, the status of tumor invasion was confirmed by pathologic examination. The CNN model was constructed to discriminate DCIS from IBC using the training and validation sets. The CNN model was evaluated using the internal and external tests, and compared with the discriminating performance using the mean ADC. The area under the curve (AUC), sensitivity, specificity, and accuracy were calculated to evaluate the performance of the previous model.ResultsThe AUCs of the ADC-based CNN model using the internal and external test sets were larger than those of the mean ADC (AUC: 0.977 vs. 0.866, P = 0.001; and 0.926 vs. 0.845, P = 0.096, respectively). Regarding the internal test set and external test set, the ADC-based CNN model yielded sensitivities of 0.893 and 0.873, specificities of 0.929 and 0.894, and accuracies of 0.907 and 0.902, respectively. Regarding the two test sets, the mean ADC showed sensitivities of 0.845 and 0.818, specificities of 0.821 and 0.829, and accuracies of 0.836 and 0.824, respectively. Using the ADC-based CNN model, the prediction only takes approximately one second for a single lesion.ConclusionThe ADC-based CNN model can improve the differentiation of IBC from DCIS with higher accuracy and less time.

To avoid exorbitant and extensive laboratory experiments, QSPR analysis, based on topological descriptors, is a very constructive statistical approach for analyzing the numerous physical and chemical properties of compounds. Therefore, we presented some new entropy measures which are based on the sum of the neighborhood degree of the vertices. Firstly, we made the partition of the edges of benzene derivatives which are based on the degree sum of neighboring vertices and then computed the neighborhood version of entropies. Secondly, we made use of the software SPSS for developing a correlation between newly introduced entropies and the physicochemical properties of benzene derivatives. Our obtained results demonstrated that the critical temperature CT, critical pressure CP, and critical volume CV can be predicted through fifth geometric arithmetic entropy, second SK entropy, and fifth ND entropy, respectively. Other remaining physical characteristics include Gibb’s energy qℰ, logP, molar refractivity ℳℛ, and Henry’s law ℋℒ that can be predicted by using sixth ND entropy.

The study preliminarily explored the sequence and difference of involvement in different neuroanatomical structures in idiopathic normal pressure hydrocephalus (INPH). We retrospectively analyzed the differences in diffusion tensor imaging (DTI) parameters in 15 ROIs [including the bilateral centrum semiovale (CS), corpus callosum (CC) (body, genu, and splenium), head of the caudate nucleus (CN), internal capsule (IC) (anterior and posterior limb), thalamus (TH), and the bilateral frontal horn white matter hyperintensity (FHWMH)] between 27 INPH patients and 11 healthy controls and the correlation between DTI indices and clinical symptoms, as evaluated by the INPH grading scale (INPHGS), the Mini-Mental State Examination (MMSE), and the timed up and go test (TUG-t), before and 1 month after shunt surgery. Significant differences were observed in DTI parameters from the CS (pFA1 = 0.004, pADC1 = 0.005) and the genu (pFA2 = 0.022; pADC2 = 0.001) and body (pFA3 = 0.003; pADC3 = 0.002) of the CC between the groups. The DTI parameters from the CS were strongly correlated with the MMSE score both pre-operatively and post-operatively. There was association between apparent diffusion coefficient (ADC) values of anterior and posterior limbs of the IC and MMSE. The DTI parameters of the head of the CN were correlated with motion, and the ADC value was significantly associated with the MMSE score. The FA value from TH correlated with an improvement in urination after shunt surgery. We considered that different neuroanatomical structures are affected differently by disease due to their positions in neural pathways and characteristics, which is further reflected in clinical symptoms and the prognosis of shunt surgery.

Delayed neurocognitive recovery (DNR) is a common subtype of postoperative neurocognitive disorders. An objective approach for identifying subjects at high risk of DNR is yet lacking. The present study aimed to predict DNR using the machine learning method based on multiple cognitive-related brain network features. A total of 74 elderly patients (≥ 60-years-old) undergoing non-cardiac surgery were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) before the surgery. Seed-based whole-brain functional connectivity (FC) was analyzed with 18 regions of interest (ROIs) located in the default mode network (DMN), limbic network, salience network (SN), and central executive network (CEN). Multiple machine learning models (support vector machine, decision tree, and random forest) were constructed to recognize the DNR based on FC network features. The experiment has three parts, including performance comparison, feature screening, and parameter adjustment. Then, the model with the best predictive efficacy for DNR was identified. Finally, independent testing was conducted to validate the established predictive model. Compared to the non-DNR group, the DNR group exhibited aberrant whole-brain FC in seven ROIs, including the right posterior cingulate cortex, right medial prefrontal cortex, and left lateral parietal cortex in the DMN, the right insula in the SN, the left anterior prefrontal cortex in the CEN, and the left ventral hippocampus and left amygdala in the limbic network. The machine learning experimental results identified a random forest model combined with FC features of DMN and CEN as the best prediction model. The area under the curve was 0.958 (accuracy = 0.935, precision = 0.899, recall = 0.900, F1 = 0.890) on the test set. Thus, the current study indicated that the random forest machine learning model based on rs-FC features of DMN and CEN predicts the DNR following non-cardiac surgery, which could be beneficial to the early prevention of DNR.Clinical Trial Registration: The study was registered at the Chinese Clinical Trial Registry (Identification number: ChiCTR-DCD-15006096).

Objectives: Delayed neurocognitive recovery (DNR) seriously affects the post-operative recovery of elderly surgical patients, but there is still a lack of effective methods to recognize high-risk patients with DNR. This study proposed a machine learning method based on a multi-order brain functional connectivity (FC) network to recognize DNR.Method: Seventy-four patients who completed assessments were included in this study, in which 16/74 (21.6%) had DNR following surgery. Based on resting-state functional magnetic resonance imaging (rs-fMRI), we first constructed low-order FC networks of 90 brain regions by calculating the correlation of brain region signal changing in the time dimension. Then, we established high-order FC networks by calculating correlations among each pair of brain regions. Afterward, we built sparse representation-based machine learning model to recognize DNR on the extracted multi-order FC network features. Finally, an independent testing was conducted to validate the established recognition model.Results: Three hundred ninety features of FC networks were finally extracted to identify DNR. After performing the independent-sample T test between these features and the categories, 15 features showed statistical differences (P < 0.05) and 3 features had significant statistical differences (P < 0.01). By comparing DNR and non-DNR patients’ brain region connection matrices, it is found that there are more connections among brain regions in DNR patients than in non-DNR patients. For the machine learning recognition model based on multi-feature combination, the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the classifier reached 95.61, 92.00, 66.67, and 100.00%, respectively.Conclusion: This study not only reveals the significance of preoperative rs-fMRI in recognizing post-operative DNR in elderly patients but also establishes a promising machine learning method to recognize DNR.

Objective To investigate the value of the magnetic resonance neurography three-dimensional sampling perfection with application optimized contrasts by using different flip angle evolution (3D-SPACE) in displaying the neoplasms of the lumbosacral plexus. Methods MRI 3D-SPACE images of 16 cases with neoplasm in the lumbosacral plexus collected during January 2018 to December 2023 were retrospectively analyzed. All cases were confirmed by pathology. The maximum diameter of the tumors was measured and the number of lesions was counted. The location of the involved lumbosacral plexus was observed and the number of the involved nerves was counted. The relationship between the tumors and nerves was evaluated. Results Ten cases were of single lesion and 6 cases were of multiple lesions. The average maximum diameter of tumors was （57.1±37.2）mm. Single nerve involved was detected in 5 cases while multiple nerves involved were in 11 cases. The relationship between the tumor and nerve was divided into three types. The tumor grew along the nerve in 5 cases. The nerves were compressed by the tumors in 5 cases, and were invaded and surrounded in 6 cases. Conclusions MRI 3D-SPACE can reveal the location and numbers of the tumor, displaying the relationship between the tumor and nerve clearly and intuitively, which is important for localization and diagnosis of the neoplastm in the lumbosacral plexus.