Whole-liver histogram analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging for predicting progression in patients with cirrhosis.

Background: Liver cirrhosis, as the terminal phase of chronic liver disease fibrosis, is associated with high morbidity and mortality. Traditional methods for assessing liver function, such as clinical scoring systems, offer only a global evaluation and may not accurately reflect regional liver function variations. This study aimed at evaluating the diagnostic potential of whole-liver histogram analysis of gadobenate dimeglumine (Gd-BOPTA)-enhanced magnetic resonance imaging (MRI) for predicting the progression of cirrhosis. Methods: In this retrospective study, 265 consecutive patients with cirrhosis admitted to the Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University from August 2012 to September 2019 were enrolled. After the exclusion criteria were applied, 117 patients (84 males and 33 females) were divided into Child-Pugh A cirrhosis (n=43), Child-Pugh B cirrhosis (n=49), and Child-Pugh C cirrhosis (n=25). After correction for liver signal intensity with the spleen was completed, 19 histogram features of the whole liver were extracted and modeled to evaluate liver function, with the Child-Pugh class being incorporated as a clinical parameter. Receiver operating characteristic (ROC) curves were used to assess the diagnosis capability and determine the optimal cutoffs after a mean follow-up of 42.3±19.1 (range, 8-93) months. The association between significant histogram features and the cumulative incidence of hepatic insufficiency was analyzed with the adjusted Kaplan-Meier curve model. Results: Among 117 patients (12%), 14 developed hepatic insufficiency through a period of follow-up. Five features, including the median (P<0.01), 90th percentile (P<0.01), root mean squared (P<0.01), mean (P<0.01), and 10th percentile (P<0.05), were significantly different between the groups with and without hepatic insufficiency according to the Kruskal-Wallis test; in the ROC curve analysis, the area under the curve (AUC) of these features was 0.723 [95% confidence interval (CI): 0.653-0.793], 0.722 (95% CI: 0.652-0.792), 0.722 (95% CI: 0.652-0.792), 0.721 (95% CI: 0.651-0.791), and 0.674 (95% CI: 0.600-0.748) after correction, respectively (all P values <0.05). Median, 90th percentile, root mean squared, and mean were found to be significant factors in predicting liver insufficiency. The adjusted Kaplan-Meier curves revealed that patients with a feature level less than the cutoff, as compared to those with a level above the cutoff, showed a statistically shorter progression-free survival and higher incidences of hepatic insufficiency for significant features of median (cutoff =26.001; 21.28% versus 5.71%; P=0.02), 90th percentile (cutoff =86.263; 20.41% versus 5.88%; P<0.01), root mean squared (cutoff =1,028.477; 19.15% versus 7.14%; P=0.049), and mean (cutoff =27.484; 19.15% versus 7.14%; P=0.049). Patients with a 10th percentile less than -39.811 also showed a higher cumulative incidence of hepatic insufficiency than did those with a value higher than the cutoff (0.18% versus 7.46%; P=0.22). Conclusions: Whole-liver histogram analysis of Gd-BOPTA-enhanced MRI may serve as a noninvasive analytical method to predict hepatic insufficiency in patients with cirrhosis.

TopMarker: Computational screening biomarkers of hepatocellular carcinoma from transcriptome and interactome based on differential network topological parameters.

Identifying diagnostic biomarkers for cancer is crucial in the field of personalized medicine. The available transcriptome and interactome provide unprecedented opportunities and challenges for biomarker screening. From a systematic perspective, network-based medicine methods provide alternative approaches to organizing the available high-throughput omics data for deciphering molecular interactions and their associations with phenotypic states. In this work, we propose a bioinformatics strategy named TopMarker for discovering diagnostic biomarkers by comparing the network topology differences in control and disease samples. Specifically, we build up gene-gene interaction networks in the two states of control and disease respectively. The network rewiring status across the two networks results in differential network topologies reflecting dynamics and changes in normal samples when compared with those in disease. Thus, we identify the potential biomarker genes with differential network topological parameters between the control and disease gene networks. For a proof-of-concept study, we introduce the computational pipeline of biomarker discovery in hepatocellular carcinoma (HCC). We prove the effectiveness of the proposed TopMarker method using these candidate biomarkers in classifying HCC samples and validate its signature capability across numerous independent datasets. We also compare the discriminant power of biomarker genes identified by TopMarker with those identified by other baseline methods. The higher classification performances and functional implications indicate the advantages of our proposed method for discovering biomarkers from differential network topology.

DeepSAP: A Novel Brain Image-Based Deep Learning Model for Predicting Stroke-Associated Pneumonia From Spontaneous Intracerebral Hemorrhage.

RATIONALE AND OBJECTIVE: Stroke-associated pneumonia (SAP) often appears as a complication following intracerebral hemorrhage (ICH), leading to poor prognosis and increased mortality rates. Previous studies have typically developed prediction models based on clinical data alone, without considering that ICH patients often undergo CT scans immediately upon admission. As a result, these models are subjective and lack real-time applicability, with low accuracy that does not meet clinical needs. Therefore, there is an urgent need for a quick and reliable model to timely predict SAP. METHODS: In this retrospective study, we developed an image-based model (DeepSAP) using brain CT scans from 244 ICH patients to classify the presence and severity of SAP. First, DeepSAP employs MRI-template-based image registration technology to eliminate structural differences between samples, achieving statistical quantification and spatial standardization of cerebral hemorrhage. Subsequently, the processed images and filtered clinical data were simultaneously input into a deep-learning neural network for training and analysis. The model was tested on a test set to evaluate diagnostic performance, including accuracy, specificity, and sensitivity. RESULTS: Brain CT scans from 244 ICH patients (mean age, 60.24; 66 female) were divided into a training set (n = 170) and a test set (n = 74). The cohort included 143 SAP patients, accounting for 58.6% of the total, with 66 cases classified as moderate or above, representing 27% of the total. Experimental results showed an AUC of 0.93, an accuracy of 0.84, a sensitivity of 0.79, and a precision of 0.95 for classifying the presence of SAP. In comparison, the model relying solely on clinical data showed an AUC of only 0.76, while the radiomics method had an AUC of 0.74. Additionally, DeepSAP achieved an optimal AUC of 0.84 for the SAP grading task. CONCLUSION: DeepSAP's accuracy in predicting SAP stems from its spatial normalization and statistical quantification of the ICH region. DeepSAP is expected to be an effective tool for predicting and grading SAP in clinic.

Characterization of the complete chloroplast genome of Rhodiola sachalinensis and comparative analysis with its congeneric plants.

Rhodiola, belonging to the Crassulaceae family, is a perennial herbaceous plant genus. There are about 90 Rhodiola species worldwide, some of which have been reported to have medicinal properties. Rhodiola sachalinensis is a perennial medicinal herb within this genus and, in the present study, its chloroplast genome was sequenced, assembled, annotated and compared with 24 other Rhodiola species. The results obtained show that the chloroplast genome of R. sachalinensis is 151 595 bp long and has a CG content of 37.7%. The inverted repeats (IR) region of the Rhodiola chloroplast genome is the most conserved region, with the main differences being observed in the ycf1 and ndhF genes at the IRb-small single copy boundary, and rps19 and trnH genes at the IRa-large single copy boundary. Phylogenetic analysis showed that Rhodiola species form two major clades, and species with recorded medicinal properties, clustered together in one branch except for R. dumulosa. Within the genus, R. sachalinensis is most closely related to Rhodiola rosea, although comparative analyses showed that only R. sachalinensis and Rhodiola subopposita contained the psbZ gene, which encodes a highly conserved protein subunit of the Photosystem II core complex. Overall, the present study contributes to the understanding of the chloroplast genome of Rhodiola species, and provides a theoretical basis for the study of their genetic diversity and possible use as medicinal plants.

Highly selective Zn2+ near-infrared fluorescent probe and its application in biological imaging.

Zn2+ plays a vital role in regulating various life processes, such as gene expression, cell signaling, and brain function. In this study, a near-infrared fluorescent probe AXS was synthesized to detect Zn2+ with good fluorescence specificity, high selectivity, and high sensitivity; the detection limit of Zn2+ was 6.924 × 10-11 M. The mechanism of Zn2+ recognition by the AXS probe was investigated by 1H nuclear magnetic resonance titrations, UV-visible spectroscopy, fluorescence spectroscopy, Fourier-transform infrared spectroscopy, and high-resolution mass spectrometry. Test paper experiments showed that the AXS probe could detect Zn2+ in real samples. In addition, quantitative and qualitative detection of Zn2+ in common foodstuffs was achieved. For portable Zn2+ detection, a smartphone detection platform was also developed based on the AXS probe. Importantly, the AXS probe showed good bioimaging capabilities in Caenorhabditis elegans and mice.

Follicular CD8+ T cells promote immunoglobulin production and demyelination in multiple sclerosis and a murine model.

Emerging evidence underscores the importance of CD8+ T cells in the pathogenesis of multiple sclerosis (MS), but the precise mechanisms remain ambiguous. This study intends to elucidate the involvement of a novel subset of follicular CD8+ T cells (CD8+CXCR5+ T) in MS and an experimental autoimmune encephalomyelitis (EAE) murine model. The expansion of CD8+CXCR5+ T cells was observed in both MS patients and EAE mice during the acute phase. In relapsing MS patients, higher frequencies of circulating CD8+CXCR5+ T cells were positively correlated with new gadolinium-enhancement lesions in the central nervous system (CNS). In EAE mice, frequencies of CD8+CXCR5+ T cells were also positively correlated with clinical scores. These cells were found to infiltrate into ectopic lymphoid-like structures in the spinal cords during the peak of the disease. Furthermore, CD8+CXCR5+ T cells, exhibiting high expression levels of ICOS, CD40L, IL-21, and IL-6, were shown to facilitate B cell activation and differentiation through a synergistic interaction between CD40L and IL-21. Transferring CD8+CXCR5+ T cells into naïve mice confirmed their ability to enhance the production of anti-MOG35-55 antibodies and contribute to the disease progression. Consequently, CD8+CXCR5+ T cells may play a role in CNS demyelination through heightening humoral immune responses.

Trajectories of cognitive function among people aged 45 years and older living with diabetes in China: Results from a nationally representative longitudinal study (2011~2018).

OBJECTIVES: Diabetes is associated with decline of cognitive function. Exploring different trajectories of cognitive function occurring in people with diabetes is important to improved prognosis. This study aimed to investigate differential patterns of trajectories of cognitive function and baseline determinants of trajectory group membership utilizing data from middle-aged and older Chinese adults with diabetes. METHODS: Participants of the Chinese Health And Retirement Longitudinal Study (CHARLS) aged 45 years and above received biennial assessments between 2011 and 2018. The primary outcome was overall cognitive function score operationalized as sum of mental intactness and episodic memory scores derived from the Telephone Interview of Cognitive Status (TICS). A weighted growth mixture model was used to estimate cognitive function trajectories of CHARLS participants with diabetes, and baseline factors associated with trajectory group membership were investigated with weighted multinomial logistic regression. RESULTS: Data from 1,463 participants with diabetes aged 45 years and above were analyzed, a three-group trajectory model showed the best fit for overall cognitive scores: low baseline, linear declining (22.1%); moderate baseline, linear declining (37.5%) and high-stable (40.3%). Older participants, females, participants with low education, with nighttime sleep <6 h, without daytime napping habits, and with depressive symptoms were at a higher risk of unfavorable cognitive function trajectories. CONCLUSIONS: We identified heterogeneous trajectories of cognitive function among middle-aged and older people living with diabetes in China. Socially vulnerable groups including females, rural residents, and those with low education were at a higher risk for unfavorable trajectories. In health programs aimed at preventing and mitigating cognitive decline in individuals with diabetes more attention should be given to vulnerable groups. Reduced nighttime sleep, lack of daytime napping, and depressive symptoms appear to be modifiable risk factors.

Multiphase comparative study for WHO/ISUP nuclear grading diagnostic model based on enhanced CT images of clear cell renal cell carcinoma.

To compare and analyze the diagnostic value of different enhancement stages in distinguishing low and high nuclear grade clear cell renal cell carcinoma (ccRCC) based on enhanced computed tomography (CT) images by building machine learning classifiers. A total of 51 patients (Dateset1, including 41 low-grade and 10 high-grade) and 27 patients (Independent Dateset2, including 16 low-grade and 11 high-grade) with pathologically proven ccRCC were enrolled in this retrospective study. Radiomic features were extracted from the corticomedullary phase (CMP), nephrographic phase (NP), and excretory phase (EP) CT images, and selected using the recursive feature elimination cross-validation (RFECV) algorithm, the group differences were assessed using T-test and Mann-Whitney U test for continuous variables. The support vector machine (SVM), random forest (RF), XGBoost (XGB), VGG11, ResNet18, and GoogLeNet classifiers are established to distinguish low-grade and high-grade ccRCC. The classifiers based on CT images of NP (Dateset1, RF: AUC = 0.82 ± 0.05, ResNet18: AUC = 0.81 ± 0.02; Dateset2, XGB: AUC = 0.95 ± 0.02, ResNet18: AUC = 0.87 ± 0.07) obtained the best performance and robustness in distinguishing low-grade and high-grade ccRCC, while the EP-based classifier performance in poorer results. The CT images of enhanced phase NP had the best performance in diagnosing low and high nuclear grade ccRCC. Firstorder_Kurtosis and firstorder_90Percentile feature play a vital role in the classification task.

Effectiveness of Rehabilitation Interventions in Individuals With Emerging Virtual Respiratory Tract Infectious Disease: A Systematic Review and Meta-Analysis.

OBJECTIVE: Assessing rehabilitation effectiveness for persistent symptoms post-infection with emerging viral respiratory diseases. DATA SOURCES: Systematic review of seven databases (MEDLINE, EMBASE, Cochrane Library, PEDro, MedRxiv, CNKI, Wanfang) until 30 December 2023. REVIEW METHODS: Evaluated 101 studies (9593 participants) on respiratory function, exercise capacity, and quality of life. Methodological quality was assessed using the Cochrane Collaboration's Risk of Bias tool for randomized controlled trials (RCTs), the Newcastle-Ottawa Scale (NOS) for observational studies and non-RCTs, and the NIH Quality Assessment Tools for before-after studies. RESULTS: The most common rehabilitation program combined breathing exercises with aerobic exercise or strength training. Rehabilitation interventions significantly enhanced respiratory function, as evidenced by improvements on the Borg Scale (MD, -1.85; 95% CI, -3.00 to -0.70, low certainty), the mMRC Dyspnea Scale (MD, -0.45; 95% CI, -0.72 to -0.18, low certainty), and the Multidimensional Dyspnoea-12 Scale (MD, -4.64; 95% CI, -6.54 to -2.74, moderate certainty). Exercise capacity also improved, demonstrated by results from the Six-Minute Walk Test (MD, 38.18; 95% CI, 25.33-51.03, moderate certainty) and the Sit-to-Stand Test (MD, 3.04; 95% CI, 1.07-5.01, low certainty). CONCLUSION: Rehabilitation interventions are promising for survivors of viral respiratory diseases, yet gaps in research remain. Future investigations should focus on personalizing rehabilitation efforts, utilizing remote technology-assisted programs, improving research quality, and identifying specific subgroups for customized rehabilitation strategies to achieve the best outcomes for survivors.

Effectiveness of a socioecological model-guided, smart device-based, self-management-oriented lifestyle intervention in community residents: protocol for a cluster-randomized controlled trial.

BACKGROUND: Healthy lifestyles are crucial for preventing chronic diseases. Nonetheless, approximately 90% of Chinese community residents regularly engage in at least one unhealthy lifestyle. Mobile smart devices-based health interventions (mHealth) that incorporate theoretical frameworks regarding behavioral change in interaction with the environment may provide an appealing and cost-effective approach for promoting sustainable adaptations of healthier lifestyles. We designed a randomized controlled trial (RCT) to evaluate the effectiveness of a socioecological model-guided, smart device-based, and self-management-oriented lifestyles (3SLIFE) intervention, to promote healthy lifestyles among Chinese community residents. METHODS: This two-arm, parallel, cluster-RCT with a 6-month intervention and 6-month follow-up period foresees to randomize a total of 20 communities/villages from 4 townships in a 1:1 ratio to either intervention or control. Within these communities, a total of at least 256 community residents will be enrolled. The experimental group will receive a multi-level intervention based on the socioecological model supplemented with a multi-dimensional empowerment approach. The control group will receive information only. The primary outcome is the reduction of modifiable unhealthy lifestyles at six months, including smoking, excess alcohol consumption, physical inactivity, unbalanced diet, and overweight/obesity. A reduction by one unhealthy behavior measured with the Healthy Lifestyle Index Score (HLIS) will be considered favorable. Secondary outcomes include reduction of specific unhealthy lifestyles at 3 months, 9 months, and 12 months, and mental health outcomes such as depression measured with PHQ-9, social outcomes such as social support measured with the modified Multidimensional Scale of Perceived Social Support, clinical outcomes such as obesity, and biomedical outcomes such as the development of gut microbiota. Data will be analyzed with mixed effects generalized linear models with family and link function determined by outcome distribution and accounting for clustering of participants in communities. DISCUSSION: This study will provide evidence concerning the effect of a mHealth intervention that incorporates a behavioral change theoretical framework on cultivating and maintaining healthy lifestyles in community residents. The study will provide insights into research on and application of similar mHealth intervention strategies to promote healthy lifestyles in community populations and settings. TRIAL REGISTRATION NUMBER: ChiCTR2300070575. Date of registration: April 17, 2023. https://www.chictr.org.cn/index.aspx .

First Report of Sclerotinia Rot Caused by Sclerotinia sclerotiorum on Artemisia capillaris in China.

Artemisia capillaris (Asteraceae) is an annual herb found in ˃10 provinces in China. It is cultivated on ˃670 ha, with annual production around 2,500 tons. Its shoot is used in traditional Chinese medicine (Liu et al. 2021). From April to May 2023, Sclerotinia rot symptoms were seen at the Institute of Medicinal Plant Development (40.04°N, 116.28°E), Beijing, China. Disease incidence was up to 10% in the field through investigation of 300 plants. Initial symptoms were irregular tan-brown lesions (0.5 to 5.0 mm) that expended to circumferential necrosis on the roots and basal stem, aerial mycelia and sclerotia were developed on them. The leaves and stem tips were withered and droopy in severe cases. Twelve symptomatic primary roots of 12 plants from two sites were cut into 5 × 5 mm pieces, surface sterilized with 75% ethanol for 30 s and 5% NaClO for 60 s, rinsed with distilled water for three times, dried with sterile filter paper, put on potato dextrose agar (PDA), and incubated at 25°C in the dark for 2 days. Two Sclerotinia-like isolates were obtained using the hyphaltip method. White aerial mycelia were sparse and appressed for isolate YC1-3 and dense for isolate YC1-7. After incubated at 25°C in the dark for 15 days, 10 to 25 sclerotia were developed near the colony margin. Sclerotia of isolate YC1-3 were 1.0 to 3.9 × 1.2 to 4.5 (mean 1.8 × 2.2) mm (n = 60), ovoid or arc-shaped. Sclerotia of isolate YC1-7 were 1.5 to 3.4 × 2.7 to 9.2 (mean 2.3 × 4.3) mm (n = 60), ovoid, dumbbell shaped or curved. The isolates were identified as Sclerotinia sclerotiorum based on morphology (Maas 1998). To further identify the pathogens, molecular identification was performed with isolates YC1-3 and YC1-7. DNA of the two isolates were extracted by the cetyltrimethylammonium bromide (CTAB) method. Polymerase chain reaction was performed with primers ITS1/ITS4 for the internal transcribed spacer (ITS) region (Choi et al. 2020; White et al. 1990) and primers G3PDHfor/G3PDHrev for the glyceraldehyde 3-phosphate dehydrogenase (G3PDH) gene (Garfinkel. 2021). BLAST search analysis revealed that the ITS sequence (GenBank OR229758 and OR229762) was ≥99% similar to S. sclerotiorum (MN099281, MZ379265, KX781301, etc.), and the G3PDH sequence (OR778388 and OR761975) was too (MZ493894, JQ036048, OQ790148, etc.). Phylogenetic trees were computed with ITS and G3PDH sequences using the Maximum Likelihood in MEGA 11. Nine two-month-old seedlings of A. capillaris were used to test pathogenicity. The epidermis layer of each primary root was slightly wounded (2 × 2 mm, 1 mm deep) using a sterile dissecting blade. Three plants were inoculated with mycelial plugs (5 mm in diameter) of YC1-3 and YC1-7 that cultured on PDA for 7 days. Control plants were inoculated with sterile PDA plugs. All seedlings were then incubated at 25oC and 90% relative humidity. After isolate YC1-7 inoculation 3 days and isolate YC1-3 inoculation 5 days, inoculated roots had symptoms like those in the field, controls had no symptoms. S. sclerotiorum was consistently re-isolated from diseased roots, fulfilling Koch's postulates. Diseases caused by S. sclerotiorum have been reported threatens several important economical crops (Marin and Peres 2020; Guan et al. 2022). To our knowledge, this is the first report of S. sclerotiorum causes Sclerotinia rot on A. capillaris. To avoid of significant economic losses, it is urgent to establish an effective disease-management strategy.

One-step synthesis of a ZIF-8/90-based type I porous liquid.

A Type I porous liquid based on the mixed-linker zeolitic imidazolate framework, ZIF-8/90-PL, has been prepared by a one-step imine condensation reaction and characterized by X-ray diffraction, FT-IR spectroscopy, TGA and rheology analysis. This facile preparation strategy of a porous liquid has enormous industrial production and application potential, with over one kilogram of ZIF-8/90-PL being successfully prepared. ZIF-8/90-PL can be directly used as a liquid absorbent or be co-processed with alumina hollow fibers to form a composite membrane with improved selectivity in the context of CO2 separation from CH4 or N2. This simple synthesis method is expected to be extended to other metal-organic frameworks.

Radiomics-based fertility-sparing treatment in endometrial carcinoma: a review.

In recent years, with the increasing incidence of endometrial carcinoma in women of child-bearing age, to decision of whether to preserve patients' fertility during treatment has become increasingly complex, presenting a formidable challenge for both physicians and patients. Non-fertility-sparing treatment can remove lesions more thoroughly than fertility-sparing treatment. However, patients will permanently lose their fertility. In contrast, fertility-sparing treatment can treat tumors without impairing fertility, but the risk of disease progression is high as compared with non-fertility-sparing treatment. Therefore, it is extremely important to accurately identify patients who are suitable for fertility-sparing treatments. The evaluation of prognostic factors, including myometrial invasion, the presence of lymph node metastases, and histopathological type, is vital for determining whether a patient can receive fertility-sparing treatment. As a non-invasive and quantitative approach, radiomics has the potential to assist radiologists and other clinicians in determining more precise judgments with regard to the above factors by extracting imaging features and establishing predictive models. In this review, we summarized currently available fertility-sparing strategies and reviewed the performance of radiomics in predicting risk factors associated with fertility-sparing treatment. This review aims to assist clinicians in identifying patients suitable for fertility-sparing treatment more accurately and comprehensively and informs more appropriate and rigorous treatment decisions for endometrial cancer patients of child-bearing age.Critical relevance statement: Radiomics is a promising tool that may assist clinicians identify risk factors about fertility-sparing more accurately and comprehensively.

Deep learning for the rapid automatic segmentation of forearm muscle boundaries from ultrasound datasets.

Ultrasound (US) is widely used in the clinical diagnosis and treatment of musculoskeletal diseases. However, the low efficiency and non-uniformity of artificial recognition hinder the application and popularization of US for this purpose. Herein, we developed an automatic muscle boundary segmentation tool for US image recognition and tested its accuracy and clinical applicability. Our dataset was constructed from a total of 465 US images of the flexor digitorum superficialis (FDS) from 19 participants (10 men and 9 women, age 27.4 ± 6.3 years). We used the U-net model for US image segmentation. The U-net output often includes several disconnected regions. Anatomically, the target muscle usually only has one connected region. Based on this principle, we designed an algorithm written in C++ to eliminate redundantly connected regions of outputs. The muscle boundary images generated by the tool were compared with those obtained by professionals and junior physicians to analyze their accuracy and clinical applicability. The dataset was divided into five groups for experimentation, and the average Dice coefficient, recall, and accuracy, as well as the intersection over union (IoU) of the prediction set in each group were all about 90%. Furthermore, we propose a new standard to judge the segmentation results. Under this standard, 99% of the total 150 predicted images by U-net are excellent, which is very close to the segmentation result obtained by professional doctors. In this study, we developed an automatic muscle segmentation tool for US-guided muscle injections. The accuracy of the recognition of the muscle boundary was similar to that of manual labeling by a specialist sonographer, providing a reliable auxiliary tool for clinicians to shorten the US learning cycle, reduce the clinical workload, and improve injection safety.

Synergy of Paired Brønsted-Lewis Acid Sites on Defects of Zr-MIL-140A for Methanol Dehydration.

As a common defect-capping ligand in metal-organic frameworks (MOFs), the hydroxyl group normally exhibits Brønsted acidity or basicity, but the presence of inherent hydroxyl groups in the MOF structure makes it a great challenge to identify the exact role of defect-capping hydroxyl groups in catalysis. Herein, we used hydroxyl-free MIL-140A as the platform to generate terminal hydroxyl groups on defect sites via a continuous post-synthetic treatment. The structure and acidity of MIL-140A were properly characterized. The hydroxyl-contained MIL-140A-OH exhibited 4.6-fold higher activity than the pristine MIL-140A in methanol dehydration. Spectroscopic and computational investigations demonstrated that the reaction was initiated by the respective adsorption of two methanol molecules on the terminal-OH and the adjacent Zr vacancy. The dehydration of the adsorbed methanol molecules then occurred in the Brønsted-Lewis acid site co-participated associative pathway with the lowest energy barrier.

Recurrence patterns are significantly associated with the 18F­FDG PET/CT radiomic features of patients with locally advanced non­small cell lung cancer treated with chemoradiotherapy.

A model for predicting the recurrence pattern of patients with locally advanced non-small cell lung cancer (LA-NSCLC) treated with chemoradiotherapy is of great importance for precision treatment. The present study analyzed whether the comprehensive quantitative values (CVs) of the fluorine-18(18F)-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) radiomic features and metastasis tumor volume (MTV) combined with clinical characteristics could predict the recurrence pattern of patients with LA-NSCLC treated with chemoradiotherapy. Patients with LA-NSCLC treated with chemoradiotherapy were divided into training and validation sets. The recurrence profile of each patient, including locoregional recurrence (LR), distant metastasis (DM) and both LR/DM were recorded. In the training set of patients, the primary tumor prior radiotherapy with 18F-FDG PET/CT and both primary tumors and lymph node metastasis were considered as the regions of interest (ROIs). The CVs of ROIs were calculated using principal component analysis. Additionally, MTVs were obtained from ROIs. The CVs, MTVs and the clinical characteristics of patients were subjected to aforementioned analysis. Furthermore, for the validation set of patients, the CVs and clinical characteristics of patients with LA-NSCLC were also subjected to logistic regression analysis and the area under the curve (AUC) values calculated. A total of 86 patients with LA-NSCLC were included in the analysis, including 59 and 27 patients in the training and validation sets of patients, respectively. The analysis revealed 22 and 12 cases with LR, 24 and 6 cases with DM and 13 and 9 cases with LR/DM in the training and validation sets of patients, respectively. Histological subtype, CV2-5 and CV3-4 were identified as independent variables in the logistic regression analysis (P<0.05). In addition, the AUC values for diagnosing LR, DM and LR/DM were 0.873, 0.711 and 0.826, and 0.675, 0.772 and 0.708 in the training and validation sets of patients, respectively. Overall, the results demonstrated that the spatial and metabolic heterogeneity quantitative values from the primary tumor combined with the histological subtype could predict the recurrence pattern of patients with LA-NSCLC treated with chemoradiotherapy.

A brain CT-based approach for predicting and analyzing stroke-associated pneumonia from intracerebral hemorrhage.

Introduction: Stroke-associated pneumonia (SAP) is a common complication of stroke that can increase the mortality rate of patients and the burden on their families. In contrast to prior clinical scoring models that rely on baseline data, we propose constructing models based on brain CT scans due to their accessibility and clinical universality. Methods: Our study aims to explore the mechanism behind the distribution and lesion areas of intracerebral hemorrhage (ICH) in relation to pneumonia, we utilized an MRI atlas that could present brain structures and a registration method in our program to extract features that may represent this relationship. We developed three machine learning models to predict the occurrence of SAP using these features. Ten-fold cross-validation was applied to evaluate the performance of models. Additionally, we constructed a probability map through statistical analysis that could display which brain regions are more frequently impacted by hematoma in patients with SAP based on four types of pneumonia. Results: Our study included a cohort of 244 patients, and we extracted 35 features that captured the invasion of ICH to different brain regions for model development. We evaluated the performance of three machine learning models, namely, logistic regression, support vector machine, and random forest, in predicting SAP, and the AUCs for these models ranged from 0.77 to 0.82. The probability map revealed that the distribution of ICH varied between the left and right brain hemispheres in patients with moderate and severe SAP, and we identified several brain structures, including the left-choroid-plexus, right-choroid-plexus, right-hippocampus, and left-hippocampus, that were more closely related to SAP based on feature selection. Additionally, we observed that some statistical indicators of ICH volume, such as mean and maximum values, were proportional to the severity of SAP. Discussion: Our findings suggest that our method is effective in classifying the development of pneumonia based on brain CT scans. Furthermore, we identified distinct characteristics, such as volume and distribution, of ICH in four different types of SAP.

Effects of Uni- vs. Bilateral Upper Limb Robot-Assisted Rehabilitation on Motor Function, Activities of Daily Living, and Electromyography in Hemiplegic Stroke: A Single-Blinded Three-Arm Randomized Controlled Trial.

OBJECTIVE: To evaluate if bilateral or unilateral upper limb robot-assisted rehabilitation training using a new three-dimensional end-effector robot that targets shoulder and elbow flexion and abduction is superior to conventional therapy with regard to upper extremity motor function recovery and neuromuscular improvement in stroke patients. DESIGN: Randomized, controlled, parallel, assessor-blinded, three-arm clinical trial. SETTING: Southeast University Zhongda Hospital Nanjing, Jiangsu, China. METHODS: Seventy patients with hemiplegic stroke were randomly assigned to conventional training (Control, n = 23) or unilateral (URT, n = 23), or bilateral robotic training (BRT, n = 24). The conventional group received routine rehabilitation, 60 min/day, 6 days/week, for 3 weeks. For URT and BRT upper limb robot-assisted rehabilitation training was added. This was 60 min/day, 6 days/week, for 3 weeks. The primary outcome was upper limb motor function assessed with Fugl-Meyer-Upper Extremity Scale (FMA-UE). Secondary outcomes were activities of daily living (ADL) assessed with the Modified Barthel Index (MBI), Motor Evoked Potential (MEP) to assess corticospinal tract connectivity, Root Mean Square (RMS) value, and integrate Electromyography (iEMG) value recorded by surface electromyography to evaluate muscle contraction function. RESULTS: The primary outcome indicator FMA-UE (least square mean (LSMEAN): 31.40, 95% confidence interval (95% CI): 27.74-35.07) and the secondary outcome indicator MBI (LSMEAN: 69.95, 95% CI: 66.69-73.21) were significantly improved in BRT as opposed to control (FMA-UE, LSMEAN: 24.79, 95% CI: 22.23-27.35; MBI, LSMEAN: 62.75, 95% CI: 59.42-66.09); and unilateral (FMA-UE, LSMEAN: 25.97, 95% CI: 23.57-28.36; MBI, LSMEAN: 64.34, 95% CI: 61.01-67.68). BRT also showed greater improvement in the anterior deltoid bundle with regard to muscle contraction function indicated by RMS (LSMEAN: 257.79, 95% CI: 211.45-304.12) and iEMG (LSMEAN: 202.01, 95% CI: 167.09-236.94), as compared to the controls (RMS, LSMEAN: 170.77, 95% CI: 148.97-192.58; iEMG, LSMEAN: 132.09, 95% CI: 114.51-149.68), and URT (RMS, LSMEAN: 179.05, 95% CI: 156.03-202.07; iEMG, LSMEAN: 130.38, 95% CI: 107.50-153.26). There was no statistically significant difference between URT and conventional training for any outcome. There was no significant difference in MEP extraction rate after treatment between groups (p = 0.54 for URT, p = 0.08 for BRT). CONCLUSIONS: A 60 min daily training for upper extremities using a three-dimensional end-effector targeting elbow and shoulder adding conventional rehabilitation appears to promote upper limb function and ADL in stroke patients only if delivered bilaterally. URT does not seem to result in better outcomes than conventional rehabilitation. Electrophysiological results suggest that training using a bilateral upper limb robot increases the recruitment of motor neurons rather than improving the conduction function of the corticospinal tract.

Novel Reactive Distillation Process for Cyclohexyl Acetate Production: Design, Optimization, and Control.

A side-reactor column (SRC) configuration, comprising a vacuum column coupled with atmospheric side reactors, is proposed to overcome the thermodynamic restriction in the esterification of cyclohexene with acetic acid to produce cyclohexyl acetate. Meantime, this configuration can avoid the utilization of the high-pressure steam and provide enough zone for catalyst loading. In order to obtain the minimum total annual cost (TAC), the process is optimized by a mixed-integer nonlinear programming optimization method based on the improved bat algorithm. The results indicate that the optimized SRC configuration saves about 44.81% of the TAC compared to the reactive distillation process. Based on the optimized SRC process, dynamic control is carried out. The dual-point temperature and temperature-composition control structures are proposed to reject throughput and feed composition disturbances. The dynamic performances demonstrate that the temperature-composition control structure is better in maintaining product purity.

IGF2BP2 promotes colorectal cancer progression by upregulating the expression of TFRC and enhancing iron metabolism.

BACKGROUND: Colorectal cancer (CRC) is one of the most common malignant tumors of the digestive system, ranking third for morbidity and mortality worldwide. At present, no effective control method is available for this cancer type. In tumor cells, especially iron metabolization, is necessary for its growth and proliferation. High levels of iron are an important feature to maintain tumor growth; however, the overall mechanism remains unclear. METHODS: We used western blotting, immunohistochemistry (IHC) and real-time quantitative PCR to analyze the expression of IGF2BP2 in cell lines and tissues. Further, RNA-sequencing, RNA immunoprecipitation and methylated RNA immunoprecipitation experiments explored the specific binding of target genes. Moreover, the RNA stability assay was performed to determine the half-life of genes downstream of IGF2BP2. In addition, the Cell Counting Kit-8, colony formation assay, 5-ethynyl-2'-deoxyuridine assay and flow cytometry were used to evaluate the effects of IGF2BP2 on proliferation and iron metabolism. Lastly, the role of IGF2BP2 in promoting CRC growth was demonstrated in animal models. RESULTS: We observed that IGF2BP2 is associated with iron homeostasis and that TFRC is a downstream target of IGF2BP2. Further, overexpression of TFRC can rescue the growth of IGF2BP2-knockdown CRC cells. Mechanistically, we determined that IGF2BP2 regulates TFRC methylation via METTL4, thereby regulating iron metabolism and promoting CRC growth. Furthermore, using animal models, we observed that IGF2BP2 promotes CRC growth. CONCLUSION: IGF2BP2 regulates TFRC mRNA methylation via METTL4, thereby regulating iron metabolism and promoting CRC growth. Our study highlights the key roles of IGF2BP2 in CRC carcinogenesis and the iron transport pathways.