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Abstract Background The COVID-19 pandemic has significantly impacted the psychological well-being of undergraduate nursing students in China. It is vital to have an understanding of their COVID-19 phobia and its predictors, especially during transitions in public health policy. Objective This study aims to evaluate the situation and factors contributing to COVID-19 phobia among nursing students in Southwestern China. Methods A cross-sectional study was conducted in December 2022 among nursing undergraduates in southwestern China. A convenience sample of 317 undergraduate nursing students from all grades at a medical university in Chengdu was assessed using the COVID-19 Phobia Scale (C19P). Results The mean COVID-19 Phobia Scale (C19P-SC) score was 52.92 (± 13.02), indicating moderate levels of phobia, with gender, chronic disease, and perceived susceptibility being significant predictors. 11.67% of the students reported an infection history, while 81.39% knew an infected individual. Notably, fourth-year students showed significantly higher phobia level than first-year. Gender, chronic disease, perceived susceptibility, and risk significantly predicted COVID-19 phobia, explaining 16.4% of the variance Results of the thematic analysis revealed four main themes related to COVID-19 phobia and career choice among nursing undergraduates: concerns of infection risk, professional commitment, compromise and acceptance, and confronting phobia. Conclusions This study discovered a significant level of COVID-19 phobia among undergraduate nursing students and identified several risk factors, including being female, having chronic diseases, perceiving a high susceptibility to the virus, and perceiving a high level of harm after infection. These findings highlight the importance of educators focusing on the mental well-being of nursing students, particularly seniors, to reduce the impact of phobia on their social interactions and career aspirations. This will help ensure that they are well-prepared for their future roles in healthcare.

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.

Abstract Background The association between exposure to PM2.5 chemical constituents and obesity remains to be elucidated, as most studies have used a single measure of obesity. This study aimed to investigate the associations of exposure to PM2.5 chemical constituents with general, abdominal, and visceral obesity, and the mediation effect of physical activity (PA) in the associations. Methods Based on a total of 49,819 adults from the baseline of the Yunnan Behavior and Disease Surveillance cohort (YBDS) in southern China in 2021, we used multiple linear regression, weighted quantile sum regression, and quantile regression to estimate independent and joint effects of PM2.5 chemical constituents on elevated BMI, waist circumference (WC) and visceral adiposity index (VAI) and whether these effects changed in quantiles. Mediation analysis was used to examine whether physical activity acts as a mediator in these pathways. Results Per IQR μg/m3 increase in all PM2.5 chemical constituents was significantly associated with the elevated BMI (β [95% CI]: 0.170 [0.127, 0.214]), WC (0.316 [0.217, 0.415]) and VAI (0.102 [0.075, 0.129]), with the largest weights from OM (53.89, 81.67, and 89.82%, respectively). The effects of PM2.5 chemical constituents on obesity showed an overall upward trend from quantiles 1–4 of BMI, WC, and VAI, especially with a rapid upward trend from the sixth decile of VAI. Reduced PA mediated 3.16, 7.08, and 3.78% of the associations between PM2.5 chemical constituents and elevated BMI, WC, and VAI, respectively. Conclusions Exposure to PM2.5 chemical constituents, especially OM, was significantly associated with increased risks for obesity in adults. The effects of associations increased with obesity severity, with PA playing a mediation role.

Abstract 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.

The current advanced neural network models are expanding in size and complexity to achieve improved detection accuracy. This study designs a lightweight fabric defect detection algorithm based on YOLOv7-tiny, called YOLOv7-tiny-MGCK. Its objectives are to improve the performance of fabric defect detection against complex backgrounds and to find a balance between the algorithm’s lightweight nature and its accuracy. The algorithm utilizes the Mish activation function, known for its superior nonlinear performance capability and smoother curve, enabling the neural network to manage more complex challenges. The Ghost convolution module is also incorporated to reduce computation and model parameters. The lightweight upsampling technique CARAFE facilitates the flexible extraction of deep features, coupled with their integration with shallow features. In addition, an improved K-Means clustering algorithm, KMMP, is employed to select appropriate anchor box for fabric defects. The experimental results show: a reduction in the number of parameters by 45.5% and computational volume by 41.0%, along with increases in precision by 3.9%, recall by 7.0%, and mAP by 3.0%. These results indicated that the improved algorithm achieves a more effective balance between detection performance and the requirement for a lightweight solution.

Abstract 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 .

Abstract 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. Graphical abstract

Pelvic landmark detection is a significant pre-task to measure the clinical measurement in pelvic abnormality analysis. Accurate pelvic landmark detection could provide reliable clinical parameter measurement results, which are helpful for doctors to diagnose and treat pelvic diseases. However, the multi-scale characteristics, temporal diversity, and pathological abnormalities of different pelvic X-rays bring enormous challenges to the landmark detection task. In order to retain strong robustness in irregular pelvic X-rays, we propose a novel, flexible two-stage framework. In the initial stage, a single neural network is employed to estimate the locations of every landmark simultaneously, enabling the identification of potential landmark regions. Then, the receptive field of candidate region proposals is expanded by 4 times through the receptive field amplification module. In the second stage, the landmark detection module fuses semantically rich features at different scales through a multi-scale semantic fusion module. So that the framework can fully learn the strongly relevant semantic information around the landmark at high resolution. We collected a data set of 430 pelvic X-rays, including a large number of irregular pelvic X-rays, to evaluate our framework. The experimental results demonstrate that our framework achieves a state-of-the-art detection mean radial error of 3.724 ± 4.247-mm. The experimental results show that the proposed method can help doctors quickly and accurately find the characteristic points of the pelvis and could be applied to clinical diagnosis.

Abstract Here, we investigate the precoding schemes for the downlink data transmission of a time‐division duplex cell‐free massive multiple‐input multiple‐output (MIMO) system with channel aging, which arises from the user mobility. Closed‐form spectral efficiency (SE) expressions of the downlink with the normalized conjugate beamforming (NCB), and the full‐pilot zero‐forcing (FZF), are derived, which are used for the analytical system performance evaluation. Then, a novel combinatorial precoding scheme with enhanced system SE performance, which adopts either NCB or FZF according to each user channel aging condition, is proposed. Moreover, a pilot allocation strategy is proposed to alleviate the extra interference brought by the combinatorial precoding scheme. Also, a statistical channel cooperative power control is employed to further improve the performance for all the above precoding schemes. Numerical results show that the proposed precoding scheme can substantially improve the average downlink SE.

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.

IntroductionStroke-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.MethodsOur 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.ResultsOur 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.DiscussionOur 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.

Abstract Background Flowering is a critical physiological change that interferes with not only biomass yield but also secondary metabolism, such as the biosynthesis of flavonoids, in rhizome/root plants. The continuous inflorescence removal (CIR) treatment is frequently conducted to weaken this effect. Fagopyrum dibotrys (D.Don) H.Hara (Golden buckwheat) is a kind of rhizome medicinal plant rich in flavonoids and is widely used for the treatment of lung diseases. The CIR treatment is usually conducted in F. dibotrys because of its excessive reproductive growth. To uncover the molecular mechanisms, comprehensive analysis was performed using metabolome and transcriptome data obtained from normally bloomed and the CIR treated plants. Results Metabolome results demonstrated that in the rhizomes of F. dibotrys, its bioactive compound called epicatechin has higher amount than most of the detected precursors. Compared with the normally bloomed plants, the level of epicatechin in the rhizomes of the CIR group increased by 25% at the withering stage. Based on 96 samples of the control and the CIR groups at 4 flowering stages for 4 tissues, RNA-Seq results revealed a 3 ~ 5 times upregulations of all the key enzyme genes involved in the biosynthesis of epicatechin in both time (from the bud stage to the withering stage) and spatial dimensions (from the top of branch to rhizome) under the CIR treatment compared to normal flowering. Integrated analysis of LC–MS/MS and transcriptome revealed the key roles of several key enzyme genes besides anthocyanidin reductase (ANR). A total of 93 transcription factors were identified to co-expressed with the genes in epicatechin biosynthetic pathway. The flowering activator SQUAMOSA promoter-binding protein like (SPLs) exhibited opposite spatiotemporal expression patterns to that of the epicatechin pathway genes; SPL3 could significantly co-express with all the key enzyme genes rather than the flowering repressor DELLA. Weighted gene co-expression network analysis (WGCNA) further confirmed the correlations among chalcone synthases (CHSs), chalcone isomerases (CHIs), ANRs, SPLs and other transcription factors. Conclusions SPL3 might dominantly mediate the effect of normal flowering and the CIR treatment on the biosynthesis of epicatechin in rhizomes mainly through the negative regulations of its key enzyme genes including CHS, CHI and ANR.

Porous liquids are potentially useful materials for the identification and separation of non-gaseous compounds. Herein, the authors report a type II porous ionic liquid with permanent porosity and high selectivity towards l-tryptophan (l-Trp) over other aromatic amino acids.

Objective To study the mechanism of miR-30b by regulating fragile histidine triad gene(FHIT) on cell activity diffuse large B-cell lymphoma(DLBCL). Methods The tumor paraffin specimens of DLBCL patients and the paraffin markers of lymph node hyperplasia at the same time were selected from the Shanghai Minhang District Tumor Hospital (February 2017-June 2019) and divided them into the DLBCL group and the control group. DLBCL cells were divided into the WW group (DLBCL non transfected group), WZ group (DLBCL transfected NC group )and ZR group (DLBCL transfected miR-30b group). RT-qPCR method was used to detect miR-30b expression; Western blot was used to detect FHIT protein expression; colorimetric method was used to detect caspase-3 activity; Flow cytometry was used to detect DLBCL cell apoptosi and Transwell method was applied to detect cell invasion. Results The expression of miR-30b and FHIT in the control group was significantly higher than those in the DLBCL group (P＜0.05); the expression of miR-30b, FHIT and caspase-3 in the ZR group was higher than those in the WW and WZ groups (P＜0.05) The apoptosis rate of ZR group was time-dependently higher than that of WW and WZ groups(P＜0.05); the counting of DLBCL cell invasion in ZR group was significantly less than that of WW and WZ groups (P＜0.05). Conclusions After miR-30b transfection, the apoptosis of diffuse large B-cell lymphoma cells increases and the counting of invasion cells decreases. The mechanism of this result may be related to the enhancement of miR-30b, FHIT, and caspase-3 level.

With the development of society and economy in China, the Location Class of many natural gas pipelines has been upgraded compared with the as-built data. Location Class upgrading will lead to significantly increasing of failure risk in these areas. Thus, it is necessary to develop an assessment method to evaluate the continued service capacity of these pipe sections, and in order to provide a rational decision. On the basis of comparing specifications and regulations, a three-step assessment process is established, which is aimed to evaluate the pipeline step by step from the aspect of design compliance, pressure applicability, and risk acceptability, respectively. The assessment method is based on specification compliance, and is risk-oriented, which can be regarded as one of the important achievements in pipeline safety lifting phase. Besides, the best practice can be applied to other engineering in the future.

Intercropping achieved through agroforestry is increasingly being recognized as a sustainable form of land use. In agroforestry, the roots of trees and crops are intermingled, and their interactions and the production of exudates alter the soil environment and soil microbial community. Although tree–crop interactions vary depending on the stand age of the trees, how stand age affects beneficial microorganisms, including arbuscular mycorrhizal fungi (AMF), and whether changes in soil microorganisms feed back on crop growth in agroforestry systems are unknown. We therefore conducted a long-term field study to compare changes in the soil microbial and AMF communities in a jujube/wheat agroforestry system containing trees of different stand ages: 3-year-old jujube, 8-year-old jujube, and 13-year-old jujube. Our results showed that by changing soil moisture and available phosphorus content, the stand age of the trees had a significant effect on the soil microbial and AMF communities. Soil moisture altered the composition of soil bacteria, in particular the proportions of Gram-positive and Gram-negative species, and available phosphorus had significant effects on the AMF community. A network analysis showed that older stands of trees reduced both AMF diversity and network complexity. An ordinary least squares regression analysis indicated that AMF diversity, network complexity, and stability contributed to wheat yield. Finally, structural equation modeling showed that changes in edaphic factors induced by tree age brought about significant variation in the soil microbial and AMF communities, in turn, affecting crop growth. Our study highlights the crucial roles of soil microorganisms, in particular AMF, in supporting plant growth in agroforestry systems as well as the need to consider stand age in the establishment of these systems.

Background: The prognostic value of lymphadenectomy in low-grade serous ovarian cancer (LGSOC) remains uncertain. Materials and methods: A retrospective analysis of 155 patients with LGSOC who underwent surgery over a ten-year period (2011–2020) was performed. The propensity score matching (PSM) algorithm was performed between the lymphadenectomy and no lymphadenectomy groups, and Kaplan-Meier analyses were conducted to evaluate clinical prognosis. Finally, univariate and multivariate Cox proportional hazards regression analyses were performed to analyze high-risk factors associated with clinical prognosis. Results: In the pre-PSM cohort, 110 (71.0%) patients underwent lymphadenectomy. Of these, 54 (34.8%) experienced recurrence, and 27 (17.4%) died. There were statistical differences in disease-free survival (DFS) (P = 0.018) and overall survival (OS) (P = 0.016) in the post-PSM cohort. In the subgroup analysis, there were no statistically significant differences in DFS (P = 0.449) or OS (P = 0.167) in the FIGO I/II cohort. However, in the FIGO III/IV cohort, DFS (P = 0.011) and OS (P = 0.046) were statistically different between the two groups. Age > 50 years, FIGO stage III/IV, and suboptimal cytoreductive surgery were risk factors associated with prognosis. In the lymphadenectomy group, the histological status of pelvic lymph nodes had no significant effect on DFS (P = 0.205) or OS (P = 0.114). Conclusion: Lymphadenectomy was associated with DFS and OS, particularly in patients with advanced LGSOC patients. Age > 50 years, advanced FIGO stage III/IV, and suboptimal cytoreductive surgery were high-risk factors associated with clinical prognosis in patients with LGSOC.

Abstract Background Impaired axonal transport may contribute to the pathogenesis of neurodegenerative diseases, including Alzheimer’s disease (AD) and Down syndrome (DS). Axonal transport is a complex process in which specific motor proteins move cargoes to and from neuronal cell bodies and their processes. Inconsistent reports point to the changes in AD in the levels of the classical anterograde motor protein kinesin family member 5 (KIF5) and the primary neuronal KIF regulator kinesin light chain 1 (KLC1), raising the possibility that anterograde transport is compromised in AD. Methods and materials To address inconsistencies and determine if the shared pathologies in AD and elderly DS subjects with dementia (AD in DS; AD-DS) extend to the changes in KIF5 and KLC1, we measured the levels of all the three KIF5 family members and KLC1 in the AD and AD-DS frontal cortex and AD temporal cortex and cerebellum in samples taken with a short postmortem interval. To support future studies to explore the cell biological basis for any changes detected, we also examined the levels of these proteins in the brains of young and aged adult mice in the Dp (16)1Yey/+ (Dp16) mouse model of DS and J20 mouse model of AD. Results There were no changes in comparison with controls in KIF5 family members in either the AD or AD-DS samples when normalized to either β-actin or glyceraldehyde-3-phosphate dehydrogenase (GAPDH). Interestingly, however, samples from control brains as well as from AD and AD-DS demonstrated strong positive correlations between the levels of KIF5 family members, suggesting positive co-regulated expression. Importantly, while earlier reports pointed to a negative correlation between the levels of the amyloid precursor protein (APP) and KIF5A levels, we found the opposite to be true in AD-DS; this was especially striking given triplication of the APP gene, with increased APP protein levels. AD and control samples showed positive correlations between fl-hAPP and KIF5 members, but they were less consistent. In contrast to the findings for KIF5, the levels of KLC1 were downregulated in the frontal cortex of both AD and AD-DS brains; interestingly, this change was not seen in the AD temporal cortex or cerebellum. As postmortem interval has a negative effect on the levels of KLC1, but not KIF5 members, we analyzed a subset of samples with a very short postmortem interval (PMI) (≤ 6 h), a PMI that was not significantly correlated with the levels of KLC1 in either AD or AD-DS samples; we confirmed the presence of a statistically significant reduction of KLC1 in AD and AD-DS brains as compared with control brains. Studies comparing Dp16 to its euploid control recapitulated human studies in demonstrating no change in KIF5 levels and a positive correlation between the levels of KIF5 family members. J20 mice also showed normal KIF5 levels. However, unlike the AD and AD-DS frontal cortex, KLC1 levels were not reduced in the brains of Dp16 or J20 mice. Conclusion These data point to significant reductions in KLC1 in AD and AD-DS. In so doing, they raise the possibility of compromised KLC1-mediated axonal transport in these conditions, a posit that can now be pursued in model systems in which KLC1 expression is reduced.

Resins have enormous potential in the removal of naphthenic acids (NAs) from transformer oil due to their rich porosity and high mechanical and diversified functionality, whereas their poor adsorption capacity limits application. In this work, the polystyrene–diethylamine resin (PS−DEA−x) was prepared by grafting diethylamine (DEA) onto chloromethylated polystyrene (PS−Cl) resin to efficiently adsorb cyclopentane carboxylic acid from transformer oil for the first time. The characterization analysis results indicated that amine contents were significantly enhanced with the increase in DEA. Particularly, resin with a molar ratio of 1:5 depending on chloromethyl to DEA (PS−DEA−5) exhibited the highest amine contents and efficient adsorption of cyclopentane carboxylic acid (static adsorption capacity up to 110.0 mg/g), which was about 5 times higher than that of the pristine PS−Cl. The thermodynamic and kinetic studies showed that the adsorption behaviors could be well fitted to the Langmuir isotherm equation and pseudo−second−order rate equation. Moreover, it was found that 1 g of the PS−DEA−5 can decontaminate about 760 mL transformer oil to meet reuse standards by a continuous stream, indicating its potential application in industry.

The transportation buildings alongside the expressways (TBE) have comprehensive characteristics, providing shopping and accommodations for drivers and passengers. However, the indoor thermal environment and energy consumption of such service buildings was not covered in most studies. To this end, based on some typical TBEs, this study investigated the thermal environment and energy consumption characteristics for TBEs. And the mentioned TBEs are located in Xiong'an New Area, a national special zone with requirements of low carbon and low energy consumption in China's cold region. The thermal environment study included questionnaire survey and on-site investigation by adopting dynamic thermal comfort evaluation index (i.e., Relative Warmth Index (RWI) and Heat Deficit Rate (HDR)). Then, the TBE energy consumption was investigated with the main influencing factor analyses. Finally, numerical simulations were conducted to analyze the energy efficiency approaches in TBE. The results showed that RWI and HDR were able to evaluate the thermal comfort of personnel in transitional environment of TBE in winter. Meanwhile, when the room temperature was set as 16 °C, it was still able to maintain the thermal environment for the indoor staff. The main energy influencing factors of TBEs are building scale, system equipment and usage characteristics. Besides, it was practicable to adopt the heat pump system to replace conventional space heating and cooling system, of which the total energy consumption of geothermal heat pump reduced by 38.1%.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive solid tumors in the digestive system. A greater understanding of the pathogenesis of PDAC may facilitate the search for new therapeutic targets. Guanine nucleotide‐binding protein subunit gamma‐12 (GNG12) belongs to the G protein family and participates in the modulation of the inflammatory signaling cascade. However, the cancer‐related function and clinical relevance of GNG12 in PDAC have not previously been reported. Here, we investigated the clinical significance of GNG12 in PDAC using the Oncomine web tool, the gene expression profiling interactive analysis tool and tissue microarray (TMA). GNG12 expression was observed to be higher in PDAC patient specimens than in nontumor pancreatic tissues, and high expression of GNG12 was associated with poor prognosis. We subsequently show that GNG12 promotes pancreatic cancer cell growth in vivo and in vitro, as evaluated using 3‐(4,5‐dimethylthiazol‐2‐yl)‐5‐(3‐carboxymethoxyphenyl)‐2‐(4‐sulfophenyl)‐2H‐tetrazolium, inner salt assays, colony formation assays and a xenograft mouse model. Furthermore, our results suggest that GNG12 activates nuclear factor‐κB signaling and modulates the immune response. Collectively, our findings suggest that GNG12 may be suitable as a new prognosis‐related biomarker and a promising target for treatment of pancreatic cancer.

In this paper, we present the detailed rate analysis for cell-free massive multiple-input multiple-output (MIMO) systems over spatially correlated Rayleigh fading channels. Taking the realistic impairment effects of spatial channel correlation, pilot contamination, and channel estimation errors into account, the lower-bounds of the achievable rates for both the uplink and downlink are derived with the low-complexity linear processing such as matched filter and conjugate beamforming, which enable us to take cognizance of the impacts of transmitted power, and the number of access points (APs). Based on the derived rate results, the asymptotic performance analysis is then carried out. Besides, we propose the sophisticated max-min power allocation strategies taking the actual requirements into consideration to provide uniformly good service to all users. However, the objective functions of the two optimization problems are both non-concave. Fortunately, the former for uplink can be characterized as geometric programming (GP), whilst the latter for downlink merging the efficient tools of second-order-cone programming (SOCP). Lastly, the numerical results are shown to verify our analytical results and the effectiveness of the proposed max-min fairness algorithms.

In millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, the hybrid beamforming architecture has been put forward to reduce the high hardware cost and power consumption, which are resulted from the tremendous requirements of dedicated radio frequency (RF) chains. In this paper, we propose several strategies to design analog and digital precoders for a point-to-point (P2P) hybrid MIMO system. Aiming at minimizing the Euclidean distance between the optimal digital precoder and hybrid precoder, we decouple this matrix factorization problem into a nonconvex quadratically constrained quadratic programming (QCQP) problem and an unit-modulus least-squares (ULS) problem, which can be solved by the presented three alternating optimization algorithms. Simulation and analysis results indicate that the proposed semidefinite relaxation based alternating optimization (SDR-AO) algorithm can approach near-optimal spectral efficiency performance compared with previous algorithms in the literature, but shows extremely high computational complexity. The alternating direction method of multipliers based alternating optimization (ADMM-AO) algorithm is preferred in the case that the number of transmit antennas is much larger than that of receive antennas or the amount of data streams is small. Moreover, when equal number of RF chains and data streams are employed, the analytical constant modulus factorization based alternating optimization (ACMF-AO) algorithm is a better choice. Finally, the proposed algorithms can also be well applied in finite resolution phase shifters (PSs) of the analog component and are extended to wideband mmWave systems.

lncRNA–protein interactions play essential roles in a variety of cellular processes. However, the experimental methods for systematically mapping of lncRNA–protein interactions remain time-consuming and expensive. Therefore, it is urgent to develop reliable computational methods for predicting lncRNA–protein interactions. In this study, we propose a computational method called LncPNet to predict potential lncRNA–protein interactions by embedding an lncRNA–protein heterogenous network. The experimental results indicate that LncPNet achieves promising performance on benchmark datasets extracted from the NPInter database with an accuracy of 0.930 and area under ROC curve (AUC) of 0.971. In addition, we further compare our method with other eight state-of-the-art methods, and the results illustrate that our method achieves superior prediction performance. LncPNet provides an effective method via a new perspective of representing lncRNA–protein heterogenous network, which will greatly benefit the prediction of lncRNA–protein interactions.

Down syndrome (DS) is the most common genetic cause of Alzheimer’s disease (AD) due to trisomy for all or part of human chromosome 21 (Hsa21). It is also associated with other phenotypes including distinctive facial features, cardiac defects, growth delay, intellectual disability, immune system abnormalities, and hearing loss. All adults with DS demonstrate AD-like brain pathology, including amyloid plaques and neurofibrillary tangles, by age 40 and dementia typically by age 60. There is compelling evidence that increased APP gene dose is necessary for AD in DS, and the mechanism for this effect has begun to emerge, implicating the C-terminal APP fragment of 99 amino acid (β-CTF). The products of other triplicated genes on Hsa21 might act to modify the impact of APP triplication by altering the overall rate of biological aging. Another important age-related DS phenotype is hearing loss, and while its mechanism is unknown, we describe its characteristics here. Moreover, immune system abnormalities in DS, involving interferon pathway genes and aging, predispose to diverse infections and might modify the severity of COVID-19. All these considerations suggest human trisomy 21 impacts several diseases in an age-dependent manner. Thus, understanding the possible aging-related mechanisms associated with these clinical manifestations of DS will facilitate therapeutic interventions in mid-to-late adulthood, while at the same time shedding light on basic mechanisms of aging.

Background: Oral squamous cell carcinoma (OSCC) is one of the most common malignant tumors worldwide. Patients with poorly differentiated OSCC often exhibit a poor prognosis. AUNIP (Aurora Kinase A and Ninein Interacting Protein), also known as AIBp, plays a key role in cell cycle and DNA damage repair. However, the function of AUNIP in OSCC remains elusive. Methods: The differentially expressed genes (DEGs) were obtained using R language. Receiver operating characteristic curve analysis was performed to identify diagnostic markers for OSCC. The effectiveness of AUNIP in diagnosing OSCC was evaluated by machine learning. AUNIP expression was analyzed in publicly available databases and clinical specimens. Bioinformatics analysis and in vitro experiments were conducted to explore biological functions and prognostic value of AUNIP in OSCC. Findings: The gene integration analysis revealed 90 upregulated DEGs. One candidate biomarker, AUNIP, for the diagnosis of OSCC was detected, and its expression gradually increased along with malignant differentiation of OSCC. Bioinformatics analysis demonstrated that AUNIP could be associated with tumor microenvironment, human papillomavirus infection, and cell cycle in OSCC. The suppression of AUNIP inhibited OSCC cell proliferation and resulted in G0/G1 phase arrest in OSCC cells. The survival analysis showed that AUNIP overexpression predicted poor prognosis of OSCC patients.Interpretation: AUNIP could serve as a candidate diagnostic and prognostic biomarker for OSCC and suppression of AUNIP may be a potential approach to preventing and treating OSCC. Fund: Taishan Scholars Project in Shandong Province (ts201511106) and the National Natural Science Foundation of China (Nos. 61603218). Keywords: Oral squamous cell carcinoma, Receiver operating characteristic curve, Biomarker, Weighted gene co-expression network analysis, Survival analysis, AUNIP

Early detection of gastrointestinal tumors improves patient survival. However, patients with these tumors are typically diagnosed at an advanced stage and have poor prognosis. The incidence and mortality of gastrointestinal cancers, including esophageal, gastric, liver, colorectal, and pancreatic cancers, are increasing worldwide. Novel diagnostic and therapeutic agents are required to improve patient survival and quality of life. The tumor microenvironment, which contains nontumor cells, signaling molecules such as growth factors and cytokines, and extracellular matrix proteins, plays a critical role in cancer cell proliferation, invasion, and metastasis. Transforming growth factor beta (TGF-β) signaling has dual roles in gastrointestinal tumor development and progression as both a tumor suppressor and tumor promoter. Here, we review the dynamic roles of TGF-β and its receptors in gastrointestinal tumors and provide evidence that targeting TGF-β signaling may be an effective therapeutic strategy.

A novel ground penetrating radar (GPR)-based subsurface water content classification and prediction technique using deep neural networks is proposed. The fantastic advantages of deep network in classification and regression tasks show the huge potential to measure soil moisture content status quickly after the network trained. The technique is based on convolutional neural network (CNN), and does not need to extract data features in advance. We design two CNNs for the classification and regression tasks separately. Both networks can be divided into two parts: convolutional layers following max-pooling layers to extract features, and classifier or regressor part for output prediction. One network's output is a classification prediction (moisture quantitative classification), the other is prediction of a continuous variable (water content). We train the classification network firstly, and then use transfer learning technique to reuse the feature extraction part to train the regression network. With application of transfer learning, the regression task needs less training dataset and can achieve a good performance. Our method can be applied on many field, such as roadbed maintenance and continuous soil water content estimation.

Small cell lung cancer (SCLC) is one of the most common types of malignant tumors, characterized by rapid growth and early metastasis spread. Early and accurate diagnosis of SCLC is vital for improved survival. Accurate cancer segmentation helps doctors understand the location and size of cancer and make better diagnostic decisions. However, manual segmentation of lung cancers from large amounts of medical images is a time-consuming and challenging task. In this paper, we propose a hybrid segmentation network (referred to as HSN) based on convolutional neural network (CNN) to automatically segment SCLC from computed tomography (CT) images. The design philosophy of our model is to combine a lightweight 3D CNN to learn long-range 3D contextual information and a 2D CNN to learn fine-grained semantic information, which is essential for accurate cancer segmentation. We propose a hybrid features fusion module to effectively fuse the 2D and 3D features and to jointly train these two CNNs. We utilize a generalized Dice loss function to tackle the severe class imbalance problem in data. A dataset consists of 134 CT scans was constructed to evaluate our model. Our model achieved high performances with a mean Dice score of 0.888, a mean sensitivity score of 0.872 and a mean precision of 0.909, outperforming the other state-of-the-art 2D and 3D CNN methods by a large margin.

This paper considers an uplink cell-free massive multi-input multi-output (mMIMO) system with multi-antenna access points (APs) and users, assuming low-resolution analog-to-digital converter (ADC) architecture is employed at the APs. Leveraging on the additive quantization noise model (AQNM), we derive a tight approximate expression for uplink spectral efficiency (SE). This trackable finding provides us with a tool for easily quantifying the impacts of the number of antenna arrays and the number of quantization bit of low-resolution ADCs. We find 5-bit is required in a cell-free mMIMO with low-resolution ADCs to achieve the same SE as a cell-free mMIMO with full-precision ADCs. Besides, when the number of antennas of the user is small, deploying more antennas at the users can boost the sum SE. Then, to further highlight the potential of low-resolution ADCs architecture, we also investigate the tradeoff between the SE and energy efficiency (EE) with design issues surrounding the quantization bit of low-resolution ADCs and the number of antenna arrays. The resulting observations reveal that by choosing a proper quantization bit, the cell-free mMIMO with low-resolution ADCs has the capability to enjoy a better SE-EE tradeoff compared to the perfect ADCs counterpart.

BackgroundHistologic phenotype identification of Non-Small Cell Lung Cancer (NSCLC) is essential for treatment planning and prognostic prediction. The prediction model based on radiomics analysis has the potential to quantify tumor phenotypic characteristics non-invasively. However, most existing studies focus on relatively small datasets, which limits the performance and potential clinical applicability of their constructed models.MethodsTo fully explore the impact of different datasets on radiomics studies related to the classification of histological subtypes of NSCLC, we retrospectively collected three datasets from multi-centers and then performed extensive analysis. Each of the three datasets was used as the training dataset separately to build a model and was validated on the remaining two datasets. A model was then developed by merging all the datasets into a large dataset, which was randomly split into a training dataset and a testing dataset. For each model, a total of 788 radiomic features were extracted from the segmented tumor volumes. Then three widely used features selection methods, including minimum Redundancy Maximum Relevance Feature Selection (mRMR), Sequential Forward Selection (SFS), and Least Absolute Shrinkage and Selection Operator (LASSO) were used to select the most important features. Finally, three classification methods, including Logistics Regression (LR), Support Vector Machines (SVM), and Random Forest (RF) were independently evaluated on the selected features to investigate the prediction ability of the radiomics models.ResultsWhen using a single dataset for modeling, the results on the testing set were poor, with AUC values ranging from 0.54 to 0.64. When the merged dataset was used for modeling, the average AUC value in the testing set was 0.78, showing relatively good predictive performance.ConclusionsModels based on radiomics analysis have the potential to classify NSCLC subtypes, but their generalization capabilities should be carefully considered.

The acid-catalyzed Markovnikov addition of isobutene is affected by the concomitant isobutene oligomerization. Herein, a solid acid catalyst consisting of the sulfonated silica modified with crystalline ZrSiO4 was prepared, and the ZrSiO4/SiO2-SO3H catalyst went beyond conventional acid catalysts in the hydroacetoxylation of isobutene. The structure of the catalyst was well-characterized, and the role of ZrSiO4 in suppressing the dimerization of isobutene was discussed in terms of both kinetic study and in-situ DRIFT analysis. Moreover, this catalyst also showed general applicability for a broad substrate scope, further highlighting the necessity of ZrSiO4 in the solid acid catalyst.

Currently, ground penetrating radar is the major technology for the detection of urban road collapses and disaster sources. Vehicle-mounted GPR collects tens of GB of data on site every day, but the present interpretation of abnormal regions detected by radar relies on manual interpretation with low process efficiency. The abnormal region images of GPR are different from the surrounding normal images. In terms of the features of abnormal regions in GPR images with an obvious brightness change and obvious directional characteristics, an abnormal region detection algorithm based on visual attention mechanism is proposed. Firstly, the complex background noise in the GPR images is suppressed by wavelet denoising and gamma transform, and the brightness and directional characteristics of the abnormal regions are enhanced. Secondly, by building a multi-scale image brightness and orientation feature pyramid model, the features of abnormal regions of interest are continuously enhanced, and the rapid screening of abnormal regions has been achieved. The effectiveness of the algorithm has been verified by actual tests on different types of abnormal radar image data.

With the tremendous prosperity of industry, more and more hazardous waste is discharged from industrial production processes. Cresol distillation residue is a typical industrial hazardous waste that causes severe pollution without proper treatment. Herein, the co-pyrolysis of rice husk and cresol distillation residue was studied using thermogravimetry–mass spectrometry and kinetic studies. The Coats and Redfern method was employed to calculate the activation energy. The results indicated that the pyrolysis process of cresol distillation residue and RH/CDR (Rice Husk and Cresol Distillation Residue) blends can be divided into four stages and three stages for RH. The introduction of RH not only improved the thermo-stability of cresol distillation residue at a low temperature but also reduced the activation energy of the blends. The activation energy was the lowest when the proportion of rice husk in the blend was 60%. The main gaseous pyrolysis products included CH4, H2O, C2H2, CO2, C3H6 and H2. There existed an unusual combination of synergistic and inhibitive interactions between RH and cresol distillation residue, respectively, within different temperature ranges. The synergistic interaction decreased the reaction’s activation energy, whereas the inhibitive interaction reduced the emission of main gaseous products, such as CH4 and CO2. It was concluded that the addition of RH was conducive to improving the pyrolytic performance of cresol distillation residue and the resource utilization of cresol distillation residue.

Chronic cerebral hypoperfusion (CCH) is considered a preclinical condition of mild cognitive impairment and thought to precede dementia. However, as the principal cholinergic source of hippocampus, whether the septo-hippocampal neurocircuit was impaired after CCH is still unknown. In this study, we established the CCH rat model by bilateral common carotid artery occlusion (2VO). Under anesthesia, the medial septum (MS) of rats was stimulated to evoke the field excitatory post-synaptic potential (fEPSP) in the pyramidal cell layer of dCA1. Consequently, we observed decreased amplitude of fEPSP and increased paired-pulse ratio (PPR) after 8-week CCH. After tail pinch, we also found decreased peak frequency and shortened duration of hippocampal theta rhythm in 2VO rats, indicating the dysfunction of septo-hippocampal neurocircuit. Besides, by intracerebroventricularly injecting GABAergic inhibitor (bicuculline) and cholinergic inhibitors (scopolamine and mecamylamine), we found that CCH impaired both the pre-synaptic cholinergic release and the post-synaptic nAChR function in MS–dCA1 circuits. These results gave an insight into the role of CCH in the impairment of cholinergic MS–dCA1 neurocircuits. These findings may provide a new idea about the CCH-induced neurodegenerative changes.

Introduction: The International Federation of Gynecology and Obstetrics (FIGO) staging system is considered the most powerful prognostic factor in patients with cervical cancer. In addition, other surgical-pathological risk factors have been demonstrated to have significance in predicting the prognosis of patients. Therefore, the purpose of this study was to investigate the effects of the FIGO staging system and surgical-pathological risk factors on the prognosis of cervical cancer patients.Methods: A retrospective study was performed on patients diagnosed with cervical cancer at FIGO stage IB1–IIA2. Kaplan–Meier, Cox proportional hazards regression analysis and the support vector machine (SVM) algorithm were used to assess and validate the high-risk factors related to recurrence and death.Results: A total of 647 patients were included. Kaplan-Meier analysis showed that five high-risk factors, including FIGO stage, status of pelvic lymph node, parametrial involvement, tumor size, and depth of cervical cancer, had a significant effect on the prognosis of patients. In multivariate analysis, pelvic lymph node metastasis (hazard ratio [HR] 2.415, 95% confidence interval [CI] 1.471–3.965), parametrial involvement (HR 2.740, 95% CI 1.092–6.872) and >2/3 depth of cervical invasion (HR 2.263, 95% CI 1.045–4.902) were three independent risk factors of disease-free survival. Pelvic lymph node metastasis (HR 3.855, 95% CI 2.125–6.991) and parametrial involvement (HR 3.871, 95% CI 1.375–10.900) were two independent risk factors for overall survival. When all five high-risk factors were assembled and used for classification prediction through SVM, it achieved the highest prediction accuracy of recurrence (accuracy = 69.1%). The highest prediction accuracy for survival was 94.3% when only using the two independent predictors (the pathological status of lymph nodes and parametrium involvement) by SVM classifiers. Among the 13 groups of intermediate-risk factor, the combination of tumor size, histology and grade of differentiation was more accurate in predicting prognosis than the intermediate-risk factors in the Sedlis criteria (recurrence: 86.8% vs. 60.0%; death: 92.0% vs. 71.6%).Conclusions: The combination of FIGO stage and surgical-pathological risk factors can further enhance the prediction accuracy of the prognosis in patients with early-stage cervical cancer. Histology and grade of differentiation can further improve the prediction accuracy of intermediate-risk factors in the Sedlis criteria.

Background Women with congenital heart disease are considered at high risk for adverse events. Therefore, we aim to establish 2 prediction models for mothers and their offspring, which can predict the risk of adverse events occurred in pregnant women with congenital heart disease. Methods and Results A total of 318 pregnant women with congenital heart disease were included; 213 women were divided into the development cohort, and 105 women were divided into the validation cohort. Least absolute shrinkage and selection operator was used for predictor selection. After validation, multivariate logistic regression analysis was used to develop the model. Machine learning algorithms (support vector machine, random forest, AdaBoost, decision tree, k‐nearest neighbor, naïve Bayes, and multilayer perceptron) were used to further verify the predictive ability of the model. Forty‐one (12.9%) women experienced adverse maternal events, and 93 (29.2%) neonates experienced adverse neonatal events. Seven high‐risk factors were discovered in the maternal model, including New York Heart Association class, Eisenmenger syndrome, pulmonary hypertension, left ventricular ejection fraction, sinus tachycardia, arterial blood oxygen saturation, and pregnancy duration. The machine learning–based algorithms showed that the maternal model had an accuracy of 0.76 to 0.86 (area under the receiver operating characteristic curve=0.74–0.87) in the development cohort, and 0.72 to 0.86 (area under the receiver operating characteristic curve=0.68–0.80) in the validation cohort. Three high‐risk factors were discovered in the neonatal model, including Eisenmenger syndrome, preeclampsia, and arterial blood oxygen saturation. The machine learning–based algorithms showed that the neonatal model had an accuracy of 0.75 to 0.80 (area under the receiver operating characteristic curve=0.71–0.77) in the development cohort, and 0.72 to 0.79 (area under the receiver operating characteristic curve=0.69–0.76) in the validation cohort. Conclusions Two prenatal risk assessment models for both adverse maternal and neonatal events were established, which might assist clinicians in tailoring precise management and therapy in pregnant women with congenital heart disease.

Mutations in glycyl-tRNA synthetase (GlyRS) cause Charcot-Marie-Tooth disease, a neuromuscular disorder characterized by axonal degeneration. Here the authors show that mutant GlyRS interacts with histone deacetylase 6, resulting in increased deacetylation of α-tubulin and axonal transport deficits.

Posiphen tartrate (Posiphen) is an orally available small molecule that targets a conserved regulatory element in the mRNAs of amyloid precursor protein (APP) and α-synuclein (αSYN) and inhibits their translation. APP and αSYN can cause neurodegeneration when their aggregates induce neurotoxicity. Therefore, Posiphen is a promising drug candidate for neurodegenerative diseases, including Alzheimer’s disease and Parkinson’s disease. Posiphen’s safety has been demonstrated in three independent phase I clinical trials. Moreover, in a proof of concept study, Posiphen lowered neurotoxic proteins and inflammatory markers in cerebrospinal fluid of mild cognitive impaired patients. Herein we investigated whether Posiphen reduced the expression of other proteins, as assessed by stable isotope labeling with amino acids in cell culture (SILAC) followed by mass spectrometry (MS)-based proteomics. Neuroblastoma SH-SY5Y cells, an in vitro model of neuronal function, were used for the SILAC protein profiling response. Proteins whose expression was altered by Posiphen treatment were characterized for biological functions, pathways and networks analysis. The most significantly affected pathway was the Huntington’s disease signaling pathway, which, along with huntingtin (HTT) protein, was down-regulated by Posiphen in the SH-SY5Y cells. The downregulation of HTT protein by Posiphen was confirmed by quantitative Western blotting and immunofluorescence. Unchanged mRNA levels of HTT and a comparable decay rate of HTT proteins after Posiphen treatment supported the coclusion that Posiphen reduced HTT via downregulation of the translation of HTT mRNA. Meanwhile, the downregulation of APP and αSYN proteins by Posiphen was also confirmed. The mRNAs encoding HTT, APP and αSYN contain an atypical iron response element (IRE) in their 5′-untranslated regions (5′-UTRs) that bind iron regulatory protein 1 (IRP1), and Posiphen specifically bound this complex. Conversely, Posiphen did not bind the IRP1/IRE complex of mRNAs with canonical IREs, and the translation of these mRNAs was not affected by Posiphen. Taken together, Posiphen shows high affinity binding to the IRE/IRP1 complex of mRNAs with an atypical IRE stem loop, inducing their translation suppression, including the mRNAs of neurotoxic proteins APP, αSYN and HTT.

Background: T-cell acute lymphoblastic leukemia (T-ALL) is a malignant hematological disease and is often accompanied by a variety of genetic abnormalities. Hence, our study aims to investigate the relationship between MMP-2 -1306C>T and MMP-9 -1562C>T polymorphisms and the risk and prognosis of T-ALL. Methods: From April 2009 to February 2011, a total of 376 T-ALL patients were chosen as the case group. Meanwhile, 352 healthy people who passed routine health examinations were selected as the control group. A polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay was used to detect the frequency of MMP-2 -1306C>T (rs243865) and MMP-9 -1562C>T (rs3918242) polymorphisms in the study subjects. The serum levels of MMP-2 and MMP-9 were detected using enzyme-linked immunosorbent assay (ELISA). A Kaplan-Meier analysis was employed to analyze the event-free survival (EFS) rates of the T-All patients with different MMP-2 and MMP-9 genotypes. A multivariate COX model was applied to analyze the relationship between MMP-2 and MMP-9 polymorphisms and the prognosis of T-ALL patients. A C-statistic and net reclassification index (NRI) was carried out to evaluate the predictive value of MMP-2 and MMP-9 gene polymorphisms using the Cox model. Results: Compared to the control group, the genotypic frequency of MMP-2 -1306C>T (CT + TT) and MMP-9 -1562C>T (CT + TT) in the case group was significantly higher. The serum level of MMP-9 was markedly elevated in T-ALL patients with the CT + TT genotype compared to patients with the CC genotype. The results of the Kaplan-Meier analysis showed that the median EFS was lower in T-ALL patients with the CT + TT genotype of MMP-9 -1562C>T compared to patients with the CC genotype. The results of a multivariate analysis using the Cox proportional hazard model indicated that the MMP-9 -1562C>T polymorphism was associated with the prognosis of T-ALL patients. Conclusion: These results indicated that MMP-2 -1306C/T and MMP-9 -1562C/T polymorphisms might be associated with an increased risk of T-ALL. The MMP-9 -1562C>T polymorphism may also be related to the prognosis of T-ALL patients.

The composite TiO2/Mo-TiO2 were prepared by a modified sol-gel method. The prepared catalysts were characterized by X-ray diffraction, BET analysis, SEM, X-ray photoelectron spectroscopy, and UV–vis diffused reflectance spectroscopy techniques. The structural characterization results demonstrated that Mo was successfully doped into the TiO2 lattice and caused slight changes in the physiochemical properties. The UV–vis DRS showed a red shift of the adsorption edge to the visible region. The photocatalytic decomposition efficiencies of the catalysts were examined with toluene as a typical VOC in a continuous flow reactor. The photocatalytic activity of the n-n heterogeneous TiO2/Mo-TiO2 was greater than that of pure TiO2 and Mo-TiO2, and the catalyst containing a Mo/Ti mole ratio of 2.5% exhibited optimum photocatalytic properties. In general, a relative humidity of 35%, a higher oxygen content, a lower initial toluene concentration, and a higher UV intensity were beneficial for toluene decomposition.

Alzheimer disease (AD) represents an oncoming epidemic that without an effective treatment promises to exact extraordinary human and financial burdens. Studies of pathogenesis are essential for defining targets for discovering disease-modifying treatments. Past studies of AD neuropathology provided valuable, albeit limited, insights. Nevertheless, building on these findings, recent studies have provided an increasingly rich harvest of genetic, molecular and cellular data that are creating unprecedented opportunities to both understand and treat AD. Among the most significant are those documenting the presence within the AD brain of toxic oligomeric species of Aβ and tau. Existing data support the view that such species can propagate and spread within neural circuits. To place these findings in context we first review the genetics and neuropathology of AD, including AD in Down syndrome (AD-DS). We detail studies that support the existence of toxic oligomeric species while noting the significant unanswered questions concerning their precise structures, the means by which they spread and undergo amplification and how they induce neuronal dysfunction and degeneration. We conclude by offering a speculative synthesis for how oligomers of Aβ and tau initiate and drive pathogenesis. While 100 years after Alzheimer’s first report there is much still to learn about pathogenesis and the discovery of disease-modifying treatments, the application of new concepts and sophisticated new tools are poised to deliver important advances for combatting AD.

Alzheimer disease (AD) represents an oncoming epidemic that without an effective treatment promises to exact extraordinary financial and emotional burdens (Apostolova, 2016). Studies of pathogenesis are essential for defining critical molecular and cellular events and for discovering therapies to prevent or mitigate their effects. Through studies of neuropathology, genetic and cellular, and molecular biology recent decades have provided many important insights. Several hypotheses have been suggested. Documentation in the 1980s of selective loss of cholinergic neurons of the basal forebrain, followed by clinical improvement in those treated with inhibitors of acetylycholinesterase, supported the “cholinergic hypothesis of age-related cognitive dysfunction” (Bartus et al., 1982). A second hypothesis, prompted by the selective loss of cholinergic neurons and the discovery of central nervous system (CNS) neurotrophic factors, including nerve growth factor (NGF), prompted the “deficient neurotrophic hypothesis” (Chen et al., 2018). The most persuasive hypothesis, the amyloid cascade hypothesis first proposed more than 25 years ago (Selkoe and Hardy, 2016), is supported by a wealth of observations. Genetic studies were exceptionally important, pointing to increased dose of the gene for the amyloid precursor protein (APP) in Down syndrome (DS) and a familial AD (FAD) due to duplication of APP and to mutations in APP and in the genes for Presenilin 1 and 2 (PSEN1, 2), which encode the γ-secretase enzyme that processes APP (Dorszewska et al., 2016). The “tau hypothesis” noted the prominence of tau-related pathology and its correlation with dementia (Kametani and Hasegawa, 2018). Recent interest in induction of microglial activation in the AD brain, as well as other manifestations of inflammation, supports the “inflammatory hypothesis” (Mcgeer et al., 2016). We place these findings in the context of the selective, but by no means unique, involvement of BFCNs and their trophic dependence on NGF signaling and speculate as to how pathogenesis in these neurons is initiated, amplified and ultimately results in their dysfunction and death. In so doing we attempt to show how the different hypotheses for AD may interact and reinforce one another. Finally, we address current attempts to prevent and/or treat AD in light of advances in understanding pathogenetic mechanisms and suggest that studies in the DS population may provide unique insights into AD pathogenesis and treatment.