Association between Copper Exposure and Cognitive Function: A Cross-Sectional Study in a County, Guangxi, China.

There has been growing attention to the impact of copper exposure on cognitive function; however, current research on the specific information regarding urinary copper and cognitive function is limited, particularly detailed analyses in the Chinese adult population. This study aimed to explore the association between copper exposure and cognitive function in a cross-sectional design. A total of 2617 participants in a county, Guangxi Zhuang Autonomous Region (Guangxi), China, were included. The mini-mental state examination (MMSE) was used to assess cognitive function, and inductively coupled plasma mass spectrometry was used to measure urinary metal levels. Spearman's rank correlation was used to analyze the correlation between urinary copper levels and various cognitive function assessment indices. After adjusting for potential confounders, binary logistic regression was used to explore the association between urinary copper levels and the risk of cognitive impairment (CI) as revealed by MMSE, and restricted cubic spline regression was further used to explore the dose-response relationship. The results showed a negative correlation between urinary copper levels and orientation, attention and calculation, memory, language ability, and MMSE total scores (P < 0.05). Compared with the low copper exposure group, the high exposure group showed a 58.5% increased risk of CI (OR = 1.585, 95%CI: 1.125 to 2.235, P = 0.008). A significant linear dose-response relationship was observed between urinary copper levels and the risk of CI (P overall = 0.045, P nonlinearity = 0.081). Our findings suggest that higher copper exposure may be associated with CI in the population of a county, Guangxi, China.

Interface States in Gate Stack of Carbon Nanotube Array Transistors.

A deep understanding of the interface states in metal-oxide-semiconductor (MOS) structures is the premise of improving the gate stack quality, which sets the foundation for building field-effect transistors (FETs) with high performance and high reliability. Although MOSFETs built on aligned semiconducting carbon nanotube (A-CNT) arrays have been considered ideal energy-efficient successors to commercial silicon (Si) transistors, research on the interface states of A-CNT MOS devices, let alone their optimization, is lacking. Here, we fabricate MOS capacitors based on an A-CNT array with a well-designed layout and accurately measure the capacitance-voltage and conductance-voltage (C-V and G-V) data. Then, the gate electrostatics and the physical origins of interface states are systematically analyzed and revealed. In particular, targeted improvement of gate dielectric growth in the A-CNT MOS device contributes to suppressing the interface state density (Dit) to 6.1 × 1011 cm-2 eV-1, which is a record for CNT- or low-dimensional semiconductors-based MOSFETs, boosting a record transconductance (gm) of 2.42 mS/µm and an on-off ratio of 105. Further decreasing Dit below 1 × 1011 cm-2 eV-1 is necessary for A-CNT MOSFETs to achieve the expected high energy efficiency.

Maturation-specific enhancements in lower extremity explosive strength following plyometric training in adolescent soccer players: A systematic review and meta-analysis.

Background: For adolescent soccer players, good sprinting and jumping abilities are crucial for their athletic performance. The application of plyometric training on boosting explosive strength in adolescent soccer players is contingent upon the maturation phase, which can mediate the training-induced adaptations. Purpose: This systematic review and meta-analysis aim to explore the maturation effect of plyometric training on the lower limb explosive power of adolescent soccer players, with vertical countermovement jump (CMJ) and 20-m sprint as the main outcome indicators. Methods: An extensive search of the literature was carried out on various databases including PubMed, Web of Science, Scopus, ProQuest, and the China National Knowledge Infrastructure (CNKI), covering the time period from the establishment of each database to February 6, 2023. The search was conducted using English keywords such as 'Plyometric,' 'Adolescent,' 'football,' and 'Explosive strength.' This study utilized the Cochrane risk of bias assessment tool to conduct a standardized quality evaluation of all the included literature. Additionally, the Review Manager 5.4 software was employed to perform data analysis on all the extracted data. Results: A total of 17 studies involving 681 adolescent soccer players aged 10 to 19 were included. Plyometric training significantly improved CMJ performance across different maturation stages, especially in the post-peak height velocity stage (POST-PHV) [MD = 4.35, 95 % CI (2.11, 6.59), P < 0.01, I2 = 60 %]. The pre-peak height velocity stage (PRE-PHV) showed the next best improvement [MD = 3.00, 95 % CI (1.63, 4.37)], while the middle-peak height velocity stage (MID-PHV) showed the least improvement [MD = 2.79, 95 % CI (1.16, 4.41), P < 0.01, I2 = 49 %]. However, improvements in 20 m sprint ability were only observed in the PRE-PHV [MD = -0.06, 95 % CI (-0.12, 0), P < 0.01, I2 = 0 %] and MID-PHV [MD = -0.18, 95 % CI (-0.27, -0.08), P < 0.01, I2 = 0 %] stages. Conclusion: Plyometric training serves as a potent strategy for boosting the lower limb explosive strength of adolescent soccer players, and the training effect is closely related to the players' biological maturity. Considering biological maturity is a key aspect that this study deems essential for the formulation of effective training programs for these adolescent players.

The architecture of silk-secreting organs during the final larval stage of silkworms revealed by single-nucleus and spatial transcriptomics.

Natural silks are renewable proteins with impressive mechanical properties and biocompatibility that are useful in various fields. However, the cellular and spatial organization of silk-secreting organs remains unclear. Here, we combined single-nucleus and spatially resolved transcriptomics to systematically map the cellular and spatial composition of the silk glands (SGs) of mulberry silkworms late in larval development. This approach allowed us to profile SG cell types and cell state dynamics and identify regulatory networks and cell-cell communication related to efficient silk protein synthesis; key markers were validated via transgenic approaches. Notably, we demonstrated the indispensable role of the ecdysone receptor (ultraspiracle) in regulating endoreplication in SG cells. Our atlas presents the results of spatiotemporal analysis of silk-secreting organ architecture late in larval development; this atlas provides a valuable reference for elucidating the mechanism of efficient silk protein synthesis and developing sustainable products made from natural silk.

Fully and Weakly Supervised Deep Learning for Meniscal Injury Classification, and Location Based on MRI.

Meniscal injury is a common cause of knee joint pain and a precursor to knee osteoarthritis (KOA). The purpose of this study is to develop an automatic pipeline for meniscal injury classification and localization using fully and weakly supervised networks based on MRI images. In this retrospective study, data were from the osteoarthritis initiative (OAI). The MR images were reconstructed using a sagittal intermediate-weighted fat-suppressed turbo spin-echo sequence. (1) We used 130 knees from the OAI to develop the LGSA-UNet model which fuses the features of adjacent slices and adjusts the blocks in Siam to enable the central slice to obtain rich contextual information. (2) One thousand seven hundred and fifty-six knees from the OAI were included to establish segmentation and classification models. The segmentation model achieved a DICE coefficient ranging from 0.84 to 0.93. The AUC values ranged from 0.85 to 0.95 in the binary models. The accuracy for the three types of menisci (normal, tear, and maceration) ranged from 0.60 to 0.88. Furthermore, 206 knees from the orthopedic hospital were used as an external validation data set to evaluate the performance of the model. The segmentation and classification models still performed well on the external validation set. To compare the diagnostic performances between the deep learning (DL) models and radiologists, the external validation sets were sent to two radiologists. The binary classification model outperformed the diagnostic performance of the junior radiologist (0.82-0.87 versus 0.74-0.88). This study highlights the potential of DL in knee meniscus segmentation and injury classification which can help improve diagnostic efficiency.

Preparation of graphene/carbon nanotube-cellulose composites assisted by ionic liquids: A review.

As is well known, cellulose, as a natural polymer material with abundant reserves, plays an irreplaceable role as the major raw material in energy and chemical-related fields. With the continuous advancement of technology, native single-component cellulose is often unsatisfactory for practical applications, constructing composites is an effective means of expanding the applications. When compounded with other ingredients to prepare composites, cellulose usually needs to be dissolved and regenerated to obtain good dispersion. Current studies have revealed that cellulose is insoluble in conventional solvents, and the limited types of solvent systems that can dissolve cellulose tend to degrade the cellulose during the dissolution process, altering the cellulose properties. Ionic liquids (ILs) are a class of solvents that are capable of dissolving cellulose without adversely affecting the cellulose during the dissolution process, such as degradation. Graphene and carbon nanotubes (CNTs) are poorly dispersed and easily agglomerated by π-π stacking in general solvents, whereas ILs can effectively shield them from π-π stacking, resulting in a favorable and steady dispersion. Thus, the cellulose composites of graphene/CNTs can be prepared with the assistance of ILs. In this paper, the solubilization of cellulose by ILs and the solubilization mechanism to the preparation of cellulose composites with graphene/CNTs are reviewed, the interactions between graphene, CNTs and cellulose in the composites are elucidated, and the preparation of cellulose composites with graphene/CNTs is introduced in terms of their structure, properties and application potential.

A Multi-Scale-Enhanced YOLO-V5 Model for Detecting Small Objects in Remote Sensing Image Information.

As a typical component of remote sensing signals, remote sensing image (RSI) information plays a strong role in showing macro, dynamic and accurate information on the earth's surface and environment, which is critical to many application fields. One of the core technologies is the object detection (OD) of RSI signals (RSISs). The majority of existing OD algorithms only consider medium and large objects, regardless of small-object detection, resulting in an unsatisfactory performance in detection precision and the miss rate of small objects. To boost the overall OD performance of RSISs, an improved detection framework, I-YOLO-V5, was proposed for OD in high-altitude RSISs. Firstly, the idea of a residual network is employed to construct a new residual unit to achieve the purpose of improving the network feature extraction. Then, to avoid the gradient fading of the network, densely connected networks are integrated into the structure of the algorithm. Meanwhile, a fourth detection layer is employed in the algorithm structure in order to reduce the deficiency of small-object detection in RSISs in complex environments, and its effectiveness is verified. The experimental results confirm that, compared with existing advanced OD algorithms, the average accuracy of the proposed I-YOLO-V5 is improved by 15.4%, and the miss rate is reduced by 46.8% on the RSOD dataset.

Bacterial Susceptibility to Ceria Nanoparticles: The Critical Role of Surrounding Molecules.

Investigating the ternary relationship among nanoparticles (NPs), their immediate molecular environment, and test organisms rather than the direct interaction between pristine NPs and test organisms has been thrust into the mainstream of nanotoxicological research. Diverging from previous work that predominantly centered on surrounding molecules affecting the toxicity of NPs by modulating their nanoproperties, this study has unveiled a novel dimension: surrounding molecules altering bacterial susceptibility to NPs, consequently impacting the outcomes of nanobio interaction. The study found that adding nitrate as the surrounding molecules could alter bacterial respiratory pathways, resulting in an enhanced reduction of ceria NPs (nanoceria) on the bacterial surfaces. This, in turn, increased the ion-specific toxicity originating from the release of Ce3+ ions at the nanobio interface. Further transcriptome analysis revealed more mechanistic details underlying the nitrate-induced changes in the bacterial energy metabolism and subsequent toxicity patterns. These findings offer a new perspective for the deconstruction of nanobio interactions and contribute to a more comprehensive understanding of NPs' environmental fate and ecotoxicity.

Correlations between growth differentiation factor 15 (GDF-15) serum levels and gene polymorphism with type 2 diabetes mellitus.

Purpose: To date, the relationship between Growth Differentiation Factor 15 (GDF-15) gene polymorphism and the risk of type 2 diabetes mellitus (T2DM) has not been clarified. Our study aims to explore the association between serum GDF-15 levels and related gene polymorphism with the risk of T2DM in a Chinese rural Yao population. Methods: This was a 1:1 case-control study with 179 T2DM patients and 179 age- and sex-matched control participants. Serum GDF-15 levels were measured by enzyme-linked immunosorbent assay, and polymorphisms (rs1059519, rs1059369, rs1804826 and rs1054564) were genotyped by MassArray mass spectrometry. Results: Serum GDF-15 (sGDF-15) levels were higher in patients with T2DM and glycosylated hemoglobin (HbA1c) ≥ 6.5 % compared to that in controls (p < 0.001). The area under the curve (AUC) corresponding to sGDF-15 levels was 0.626. Serum GDF-15 was positively correlated with fasting plasma glucose (FPG) (rs = 0.150, p < 0.001) and HbA1c (rs = 0.160, p < 0.001). The frequency of GDF-15 gene rs1054564 GC + CC genotype was significantly associated with increased risk of T2DM compared to GG genotype (OR = 1.724, 95CI: 1.046-2.841, p = 0.033). Frequencies of rs1804826 T allele (ß additive = 113.318, p = 0.026) and rs1054564 C allele (ß additive = 247.282, p = 0.001, ß dominant = 286.109, p = 0.001) was significantly correlated with higher sGDF-15. The rs1059519 C allele was negatively correlated with FPG (ß recessive = -0.607, p = 0.047) and HbA1c (ß recessive = -0.456, p = 0.020). Conclusion: Serum GDF-15 levels were positively correlated with FPG and HbA1c. The GDF-15 rs1054564 GC + CC genotype was associated with a significantly higher T2DM risk. The rs1059519 C allele was negatively correlated with FPG and HbA1c.

Inner Doping of Carbon Nanotubes with Perovskites for Ultralow Power Transistors.

Semiconducting carbon nanotubes (CNTs) are considered as the most promising channel material to construct ultrascaled field-effect transistors, but the perfect sp2 C─C structure makes stable doping difficult, which limits the electrical designability of CNT devices. Here, an inner doping method is developed by filling CNTs with 1D halide perovskites to form a coaxial heterojunction, which enables a stable n-type field-effect transistor for constructing complementary metal-oxide-semiconductor electronics. Most importantly, a quasi-broken-gap (BG) heterojunction tunnel field-effect transistor (TFET) is first demonstrated based on an individual partial-filling CsPbBr3/CNT and exhibits a subthreshold swing of 35 mV dec-1 with a high on-state current of up to 4.9 µA per tube and an on/off current ratio of up to 105 at room temperature. The quasi-BG TFET based on the CsPbBr3/CNT coaxial heterojunction paves the way for constructing high-performance and ultralow power consumption integrated circuits.

Clinical relevance of SCN and CyN induced by ELANE mutations: a systematic review.

Introduction: According to the PRISMA criteria, a systematic review has been conducted to investigate the clinical relevance between patients with severe congenital neutropenia (SCN) and cyclic congenital neutropenia (CyN) induced by ELANE mutations. Methods: We have searched PubMed, EMBASE, Web of Science, Scopus, Cochrane, CNKI, Wanfang Medicine, and VIP for ELANE mutation related literature published from 1997 to 2022. Using Microsoft Excel collect and organize data, SPSS 25, GraphPad Prism 8.0.1, and Omap analyze and plot statistical. Compare the gender, age, geography, mutation sites, infection characteristics, treatment, and other factors of SCN and CyN patients induced by ELANE mutations, with a focus on exploring the relationship between genotype and clinical characteristics, genotype and prognosis. Results: This study has included a total of 467 patients with SCN and 90 patients with CyN. The onset age of SCN and CyN are both less than 1 year old, and the onset and diagnosis age of SCN are both younger than CyN. The mutation of ELANE gene is mainly missense mutation, and hot spot mutations include S126L, P139L, G214R, c.597+1G>A. The high-frequency mutations with severe outcomes are A57V, L121H, L121P, c.597+1G>A, c.597+1G>T, S126L, C151Y, C151S, G214R, C223X. Respiratory tract, skin and mucosa are the most common infection sites, Staphylococcus aureus, Pseudomonas aeruginosa and Escherichia coli are the most common. Discussion: Patients with refractory G-CSF are more likely to develop severe outcomes. The commonly used pre-treatment schemes for transplantation are Bu-Cy-ATG and Flu-Bu-ATG. The prognosis of transplantation is mostly good, but the risk of GVHD is high. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/. PROSPERO, identifier CRD42023434656.

The Influence of Temperature on the Photoelectric Properties of GeSe Nanowires.

Using physical vapor deposition (PVD) technology, GeSe nanowires were successfully fabricated by heating GeSe powder at temperatures of 500 °C, 530 °C, 560 °C, 590 °C, and 620 °C. The microstructure, crystal morphology, and chemical composition of the resulting materials were thoroughly analyzed employing methods like Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), plus Raman Spectroscopy. Through a series of photoelectric performance tests, it was discovered that the GeSe nanowires prepared at 560 °C exhibited superior properties. These nanowires not only possessed high crystalline quality but also featured uniform diameters, demonstrating excellent consistency. Under illumination at 780 nm, the GeSe nanowires prepared at this temperature showed higher dark current, photocurrent, and photoresponsivity compared to samples prepared at other temperatures. These results indicate that GeSe nanomaterials hold substantial potential in the field of photodetection. Particularly in the visible light spectrum, GeSe nanomaterials exhibit outstanding light absorption capabilities and photoresponse.

Improving the productivity of Xinjiang cotton in heat-limited regions under two life history strategies.

Cotton is a major cash crop globally, playing a pivotal role in the textile sector. However, cotton growers in Xinjiang region are experiencing cotton yield penalty caused by limited heat environment. In this region, limited heat conditions strongly arrest cotton plant growth and development resulting in recued productivity. To counteract this problem, there is an urgent need to robustly identify efficient management strategies to improve plant performance and increase cotton yield under heat-limited situations. This will hold crucial implications for agricultural sustainability and global cotton supply. This review article identified challenges faced by cotton producers under heat limited environments with potential solutions to enhance cotton productivity. Specifically, we focused on the implementation of two life history strategies including planting early maturing and cold tolerant cultivars, and adjusting sowing date that can promote early maturity and increase cold stress tolerance. These strategies have shown promising results in protecting cotton plants from limited heat injury and consequently improved cotton productivity. By focusing on Xinjiang province unique climate and associated agronomic practices, valuable insights can be gained, which may have broader applications in other heat-limited cotton-growing regions globally. This comprehensive review endeavors to provide a foundation for future research and practical interventions aimed at boosting cotton yields under limited heat areas.

Alginate and chitosan surface coating reduces the phytotoxicity of CeO2 nanoparticles to duckweed (Lemna minor L.).

Little is known about the effect of surface coatings on the fate and toxicity of CeO2 nanoparticles (NPs) to aquatic plants. In this study, we modified nCeO2 with chitosan (Cs) and alginate (Al) to obtain positively charged nCeO2@Cs and negatively charged nCeO2@Al, respectively, and exposed them to a representative aquatic plant, duckweed (Lemna minor L.). Uncoated nCeO2 could significantly inhibit the growth of duckweed, induce oxidative damage and lead to cell death, whereas nCeO2@Cs and nCeO2@Al exhibited lower toxicity to duckweed. ICP-MS analysis revealed that the Ce content in duckweed from the nCeO2 group was 1.74 and 2.85 times higher than that in the nCeO2@Cs and nCeO2@Al groups, respectively. Microscopic observations indicated that the positively charged nCeO2@Cs was more readily adsorbed on the root surface of duckweed than the negatively charged nCeO2@Al. The results of XANES and LCF demonstrated that a certain percentage of Ce(Ⅳ) was reduced to Ce(Ⅲ) after the interaction of the three NPs with duckweed, but the degree of biotransformation differed among the treatments. Specifically, the absolute contents of Ce(III) produced of nCeO2@Cs and nCeO2@Al through biotransformation were reduced by 55.5% and 83.5%, respectively, compared with that of the nCeO2 group, which might be the key factor for the diminished phytotoxicity of the coated nCeO2 to the duckweed. These findings were valuable for understanding the toxicity of metal-based NPs to aquatic plants and for the synthesis of environmentally friendly nanomaterials.

Evaluation of post-activation mandibular remodelling in children with craniofacial microsomia treated with distraction osteogenesis.

OBJECTIVE: Patients with type IIA craniofacial microsomia (CFM) may benefit from mandibular distraction osteogenesis (MDO) treatment during childhood; however, remodelling of the mandible during the consolidation phase, which may affect the short-term outcomes of MDO, has not yet been quantitatively analysed using computed tomography. Therefore, we aimed to investigate bone remodelling of the mandible in children with type IIA CFM treated with MDO before distractor removal and the factors that influence ramus vertical elongation efficiency. MATERIALS AND METHODS: Twenty-three children with unilateral CFM were studied between 2020 and 2024. Longitudinal computed tomography data (preoperative, end of active phase and at pre-distractor removal) were analysed. Condyle positions and the mandibular cant were analysed using a paired-sample t test. The relapse rates of vertical lengthening and mandibular cant were calculated. The correlation between distraction efficiency and preoperative craniofacial morphology was analysed. RESULTS: The condyle on the affected side moved upwards and backwards by 28.84 ± 4.08 and 2.85 ± 4.33 mm, respectively during the active phase but lost 7.66 ± 2.64 mm of vertical extension during the consolidation phase. The relapse rates for vertical extension of the condyle and occlusal plane were 27% and 35%, respectively. The ratio of mandibular ramus height was positively related to EV. CONCLUSIONS: In children with CFM, attention should be paid to vertical elongation instability and relapse of mandibular inclination during consolidation. Severe mandibular ramus hypoplasia is a preoperative risk factor for vertical skeletal relapse during consolidation. Further efforts are required to reduce the stress that leads to relapse.

UPLC-Q-TOF-MS/MS combined with machine learning methods for screening quality indicators of Hypericum perforatum L.

Hypericum perforatum L. (HPL), also known as St. John's wort, is one of the extensively researched domestically and internationally as a medicinal plant. In this study, non-targeted metabolomics combined with machine learning methods were used to identify reasonable quality indicators for the holistic quality control of HPL. First, the high-resolution MS data from different samples of HPL were collected, and visualized the chemical compounds through the MS molecular network. A total of 122 compounds were identified. Then, the orthogonal partial least squares-discriminant analysis (OPLS-DA) model was established for comparing the differences in metabolite expression between flower, leaf, and branches. A total of 46 differential metabolites were screened out. Subsequently, analyzing the pharmacological activities of these differential metabolites based on protein-protein interaction (PPI) network. A total of 25 compounds associated with 473 gene targets were retrieved. Among them, 13 highly active compounds were selected as potential quality markers, and five compounds were ultimately selected as quality control markers for HPL. Finally, three different classifiers (support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN)) were used to validate whether the selected quality control markers are qualified. When the feature count is set to 122 and 46, the RF model demonstrates optimal performance. As the number of variables decreases, the performance of the RF model degrades. The KNN model and the SVM model also exhibit a decrease in performance but still manage to satisfy the intended requirements. The strategy can be applied to the quality control of HPL and can provide a reference for the quality control of other herbal medicines.

Central Roles of ZmNAC128 and ZmNAC130 in Nutrient Uptake and Storage during Maize Grain Filling.

Grain filling is critical for determining yield and quality, raising the question of whether central coordinators exist to facilitate the uptake and storage of various substances from maternal to filial tissues. The duplicate NAC transcription factors ZmNAC128 and ZmNAC130 could potentially serve as central coordinators. By analyzing differentially expressed genes from zmnac128 zmnac130 mutants across different genetic backgrounds and growing years, we identified 243 highly and differentially expressed genes (hdEGs) as the core target genes. These 243 hdEGs were associated with storage metabolism and transporters. ZmNAC128 and ZmNAC130 play vital roles in storage metabolism, and this study revealed two additional starch metabolism-related genes, sugary enhancer1 and hexokinase1, as their direct targets. A key finding of this study was the inclusion of 17 transporter genes within the 243 hdEGs, with significant alterations in the levels of more than 10 elements/substances in mutant kernels. Among them, six out of the nine upregulated transporter genes were linked to the transport of heavy metals and metalloids (HMMs), which was consistent with the enrichment of cadmium, lead, and arsenic observed in mutant kernels. Interestingly, the levels of Mg and Zn, minerals important to biofortification efforts, were reduced in mutant kernels. In addition to their direct involvement in sugar transport, ZmNAC128 and ZmNAC130 also activate the expression of the endosperm-preferential nitrogen and phosphate transporters ZmNPF1.1 and ZmPHO1;2. This coordinated regulation limits the intake of HMMs, enhances biofortification, and facilitates the uptake and storage of essential nutrients.

A Semi-Automatic Magnetic Resonance Imaging Annotation Algorithm Based on Semi-Weakly Supervised Learning.

The annotation of magnetic resonance imaging (MRI) images plays an important role in deep learning-based MRI segmentation tasks. Semi-automatic annotation algorithms are helpful for improving the efficiency and reducing the difficulty of MRI image annotation. However, the existing semi-automatic annotation algorithms based on deep learning have poor pre-annotation performance in the case of insufficient segmentation labels. In this paper, we propose a semi-automatic MRI annotation algorithm based on semi-weakly supervised learning. In order to achieve a better pre-annotation performance in the case of insufficient segmentation labels, semi-supervised and weakly supervised learning were introduced, and a semi-weakly supervised learning segmentation algorithm based on sparse labels was proposed. In addition, in order to improve the contribution rate of a single segmentation label to the performance of the pre-annotation model, an iterative annotation strategy based on active learning was designed. The experimental results on public MRI datasets show that the proposed algorithm achieved an equivalent pre-annotation performance when the number of segmentation labels was much less than that of the fully supervised learning algorithm, which proves the effectiveness of the proposed algorithm.

Comparative efficacy and safety of six angiotensin II receptor blockers in hypertensive patients: a network meta-analysis.

BACKGROUND: The antihypertensive effects of angiotensin II receptor blockers (ARBs) are well recognized. However, conventional meta-analyses have reported inconsistent results on their efficacy and safety. AIM: This study aimed to evaluate the efficacy and safety of six ARBs (losartan, valsartan, irbesartan, telmisartan, candesartan, and olmesartan) commonly used to treat hypertension, using a network meta-analysis. METHOD: We retrieved randomized controlled trials on hypertension treatment using ARBs from the PubMed, Embase, Cochrane Library, CNKI, and Wanfang databases. The efficacy outcomes included absolute changes in office systolic and diastolic blood pressure from baseline, and 24-h ambulatory blood pressure. Safety outcomes were assessed by the total number of adverse events (AEs) during treatment. We conducted the network meta-analysis using the 'bugsnet' and 'gemtc' packages in R. RESULTS: A total of 193 studies were included. Olmesartan had the highest surface under the cumulative ranking in reducing office systolic (91.4%) and diastolic blood pressure (87.2%). Candesartan has the highest ranking in lowering 24 h ambulatory systolic blood pressure (95.4%), while telmisartan reduced 24 h ambulatory diastolic blood pressure (83.4%). Olmesartan also ranked highest in safety (70.8%). CONCLUSION: Valsartan and losartan were less effective in lowering blood pressure than other drugs, with no significant differences. Olmesartan and telmisartan were associated with fewer AEs than losartan, although the incidence of adverse events was similar between the other blockers. Olmesartan and telmisartan demonstrated the best balance of antihypertensive efficacy and minimal adverse events. More research is needed to confirm whether telmisartan and olmesartan are optimal choices for controlling blood pressure in patients.