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Abstract Background Obstructive sleep apnea (OSA) is a pervasive, chronic sleep-related respiratory condition that causes brain structural alterations and cognitive impairments. However, the causal association of OSA with brain morphology and cognitive performance has not been determined. Methods We conducted a two-sample bidirectional Mendelian randomization (MR) analysis to investigate the causal relationship between OSA and a range of neurocognitive characteristics, including brain cortical structure, brain subcortical structure, brain structural change across the lifespan, and cognitive performance. Summary-level GWAS data for OSA from the FinnGen consortium was used to identify genetically predicted OSA. Data regarding neurocognitive characteristics were obtained from published meta-analysis studies. Linkage disequilibrium score regression analysis was employed to reveal genetic correlations between OSA and related traits. Results Our MR study provided evidence that OSA was found to significantly increase the volume of the hippocampus (IVW β (95% CI) = 158.997 (76.768 to 241.227), P = 1.51e-04), with no heterogeneity and pleiotropy detected. Nominally causal effects of OSA on brain structures, such as the thickness of the temporal pole with or without global weighted, amygdala structure change, and cerebellum white matter change covering lifespan, were observed. Bidirectional causal links were also detected between brain cortical structure, brain subcortical, cognitive performance, and OSA risk. LDSC regression analysis showed no significant correlation between OSA and hippocampus volume. Conclusions Overall, we observed a positive association between genetically predicted OSA and hippocampus volume. These findings may provide new insights into the bidirectional links between OSA and neurocognitive features, including brain morphology and cognitive performance.

The escalating salinization of cultivated soil poses a significant threat to the ecological environment. It is imperative to establish a monitoring system and mitigate the spread of salinization in arid and coastal areas through remote sensing, incorporating high-precision cross-regional models for soil salt content inversion. This study focuses on typical saline-alkali soils in arid and coastal regions of China. Using Sentinel 2 data (including 6 bands and 27 spectral indices), along with soil texture, moisture content, temperature, precipitation, and digital elevation model (DEM) data to establish an arid-coastal salinity inversion model. Variable selection methods such as pearson correlation coefficient (PCC), variable importance in projection (VIP), gray relational analysis (GRA), and gradient boosting machine (GBM) were used, while using 9 models including adaptive boosting (Adaboost), extremely randomized trees (ERT), and light gradient boosting machine (LightGBM). The best model was further elucidated using the Shapley additive explanations method. Results indicate that the common sensitive characteristic variables of arid-coastal areas were spectral indices and soil properties in PCC, the spectral variable bands and indices in VIP, and all variables in GRA and GBM. The best inversion model GBM-ERT (R2 = 0.91, RMSE = 1.06) in arid areas exhibited higher accuracy than the best inversion model GBM-Adaboost (R2 = 0.77, RMSE = 1.74) in coastal areas. The arid-coastal inversion model PCC-LightGBM demonstrated the best inversion performance (R2 = 0.64, RMSE = 2.29) and simulation performance in arid (R2 = 0.67) and coastal areas (R2 = 0.63). Dead fuel index (DFI) had the most significant impact on model prediction (0.89) and the second ratio index (RI2) contributed the highest relative importance (18 %) to the model. Our analysis indicates that the arid-coastal model of PCC-LightGBM established using common characteristic variables, can effectively monitor large-scale soil salinity.

ObjectiveNutritional intervention prior to the occurrence of cachexia will significantly improve the survival rate of lung cancer patients. This study aimed to establish an ensemble learning model based on anthropometry and blood indicators without information on body weight loss to identify the risk factors of cachexia for early administration of nutritional support and for preventing the occurrence of cachexia in lung cancer patients.MethodsThis multicenter study included 4,712 lung cancer patients. The least absolute shrinkage and selection operator (LASSO) method was used to obtain the key indexes. The characteristics excluded weight loss information, and the study data were randomly divided into a training set (70%) and a test set (30%). The training set was used to select the optimal model among 18 models and verify the model performance. A total of 18 machine learning models were evaluated to predict the occurrence of cachexia, and their performance was determined using area under the curve (AUC), accuracy, precision, recall, F1 score, and Matthews correlation coefficient (MCC).ResultsAmong 4,712 patients, 1,392 (29.5%) patients were diagnosed with cachexia based on the framework of Fearon et al. A 17-variable gradient boosting classifier (GBC) model including body mass index (BMI), feeding situation, tumor stage, neutrophil-to-lymphocyte ratio (NLR), and some gastrointestinal symptoms was selected among the 18 machine learning models. The GBC model showed good performance in predicting cachexia in the training set (AUC = 0.854, accuracy = 0.819, precision = 0.771, recall = 0.574, F1 score = 0.658, MCC = 0.549, and kappa = 0.538). The abovementioned indicator values were also confirmed in the test set (AUC = 0.859, accuracy = 0.818, precision = 0.801, recall = 0.550, F1 score = 0.652, and MCC = 0.552, and kappa = 0.535). The learning curve, decision boundary, precision recall (PR) curve, the receiver operating curve (ROC), the classification report, and the confusion matrix in the test sets demonstrated good performance. The feature importance diagram showed the contribution of each feature to the model.ConclusionsThe GBC model established in this study could facilitate the identification of cancer cachexia in lung cancer patients without weight loss information, which would guide early implementation of nutritional interventions to decrease the occurrence of cachexia and improve the overall survival (OS).

Background: Short-term exposure to fine particulate matter (PM2.5) and its specific constituents might exacerbate allergic rhinitis (AR) conditions. However, the evidence is still inconclusive. Method: We conducted a panel study of 49 patients diagnosed with AR > 1 year prior to the study in Taiyuan, China, to investigate associations of individual exposure to PM2.5 and its constituents with oxidative parameters, symptoms, and quality of life among AR patients. All participants underwent repeated assessments of health and PM exposure at 4 time points in both the heating and nonheating seasons from June 2017 to January 2018. AR patients’ oxidative parameters were assessed using nasal lavage, and their subjective symptoms and quality of life were determined through in-person interviews using a structured questionnaire. Short-term personal exposure to PM2.5 and its constituents was estimated using the time-microenvironment-activity pattern and data from the nearest air sampler, respectively. We applied mixed-effects regression models to estimate the short-term effects of PM2.5 and its constituents. Results: The results showed that exposure to PM2.5 and its constituents, including BaP, PAHs, SO42−, NH4+, V, Cr, Cu, As, Se, Cd, and Pb, was significantly associated with increased oxidative stress, as indicated by an increase in the malondialdehyde (MDA) index. Exposure to PM2.5 and its components (V, Mn, Fe, Zn, As, and Se) was associated with decreased antioxidant activity, as indicated by a decrease in the superoxide dismutase (SOD) index. Additionally, increased visual analog scale (VAS) and rhinoconjunctivitis quality of life questionnaire (RQLQ) scores indicated that exposure to PM2.5 and its constituents exacerbated inflammatory symptoms and affected quality of life in AR patients. Conclusion: Exposure to PM2.5 and specific constituents, could exacerbate AR patients' inflammatory symptoms and adversely affect their quality of life in the heavily industrialized city of Taiyuan, China. These findings may have potential biological and policy implications.

Biodegradable calcium phosphate (CaP) ceramic coatings were prepared on self-designed Mg-2.0Zn-0.6Ca-xSi alloys by plasma electrolytic oxidation (PEO). The effect of calcium (Ca) and silicon (Si) on alloy microstructure and coating formation and biomineralization mechanisms were discussed. The in vitro bioactivity and degradability of CaP coatings were evaluated by immersion tests in simulated body fluid (SBF) solutions and trishydroxymethyl-aminomethane hydrochloric acid (Tris-HCl) buffer, respectively. The microstructure and chemical composition of the coatings, depositions and corrosion products were characterized by scanning electron microscope (SEM), transmission electron microscope (TEM), X-ray diffractometer (XRD), X-ray photoelectron spectrometer (XPS) and fourier transform infrared spectrometer (FT-IR). The electrochemical corrosion behavior of alloys and coatings was investigated using the electrochemical workstation. Results showed that the shape, quantity, size and distribution of second phases can be changed by the simultaneously addition of Ca and Si in Mg–Zn–Ca–Si alloys. The Si content in Mg-2.0Zn-0.6Ca-xSi alloy should not be higher than 0.8 wt%. The microstructure of Mg-2.0Zn-0.6Ca-xSi alloys can influence the formation and growth of PEO coating by altering the spark discharge characteristics. The plasma electrolytic oxidized Mg-2.0Zn-0.6Ca-0.8Si has potential to be served as biodegradable bone implant and cardiovascular stent.

Abstract Background The fat mass and nutritional status play important roles in the onset and progression of cancer cachexia. The present study evaluated the joint prognostic value of the fat mass, as indicated by the triceps skinfold thickness (TSF), and the serum albumin level, for mortality in patients with cancer cachexia. Methods We performed a multicentre cohort study including 5134 patients with cancer cachexia from January 2013 to April 2019. The sum of the TSF (mm) and serum albumin (g/L) was defined as the triceps skinfold–albumin index (TA). Harrell's C index, a time‐dependent receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to evaluate the prognostic performance of the TA and other indices. Optimal stratification was used to identify the thresholds to define a low TA, and the association of the TA with all‐cause mortality was evaluated using Kaplan–Meier analysis and Cox proportional hazard regression models. Results The study enrolled 2408 women and 2726 men with a median age of 58.6 years and a median follow‐up of 44 months. A total of 607 women (TA

Abstract Long‐term blood pressure variability (BPV) is a risk factor for cardiovascular diseases, dementia, and stroke. However, its genetic architecture is not fully understood. This study aims to explore its genetic factors and provide more evidence on the mechanisms and further pathological study of BPV. The genome‐wide association study (GWAS) is based on the UK Biobank cohort. There were four data collection rounds from 2006 to 2020, and 9370 participants with more than three blood pressure measurements were included. They had a median age of 55 and a male percentage of 50.1%. The phenotypes (BPV) were calculated by four methods and the genetic data contains 6 884 260 single nucleotide polymorphisms (SNPs) after imputation and quality control. A linear regression model was performed with adjustments for sex, age, genotype array, and a significant principal component. Subgroup analysis was performed on hypertension‐free participants. The significant and suggestive significant P thresholds were set as 5 × 10−8 and 1 × 10−6. Six genetic loci (BAD, CCDC88B, GPR137, PLCB3, RPS6KA4 for systolic BPV, and WWC2 for diastolic BPV) were identified by coding region SNPs at the suggestive significant P threshold (1 × 10−6). Among them, gene CCDC88B and RPS6KA4 reached the significant P threshold (5 × 10−8), with the strongest signal of SNP rs1229536170 (P = 6.36 × 10−8, β = –.29). The annotation results indicate that genes CCDC88B, GPR137, RPS6KA4, and BAD are associated with long‐term SBPV. Their functions of inflammation, epithelial dysfunction, and apoptosis are related to artery stiffness, which was reported as potential mechanisms of BPV.

Ceramic coatings with carbon nanotubes were prepared on high silicon aluminum alloy by plasma electrolytic oxidation. The microstructure, phase, and elemental compositions of the matrix and ceramic coatings were characterized by metallographic microscope, X-ray diffraction, field emission scanning electron microscope, micro confocal Raman spectrometer and energy dispersive spectrometer. The functional groups contained in the coating were characterized using Fourier infrared spectrometer and X-ray photoelectron spectroscopy. The electrochemical workstation and thermal conductivity meter were employed to study the corrosion resistance and thermal conductivity of ceramic coatings. The effect of CNTs on the morphology, microstructure, coating formation and growth, corrosion resistance and thermal conductivity of PEO coatings on ADC12 Al alloy was investigated. The results showed that the micropores on the surface of the PEO coating can be used as discharge channels for various migrating ions and particle groups in the electrolyte to participate in the formation and inward and outward growth of the coatings. CNTs participated in the formation and growth of ceramic coating by surface adsorption, melt wrapping and fusion reaction during the plasma electrolytic oxidation process. The coating contains mainly α-Al2O3, γ-Al2O3, SiO2 and small amount of SiC phases. The changes of electrochemical corrosion and thermal conductivity of the PEO coatings are related to the regulation of the coating microstructure by CNTs.

Storage duration detection for frozen meat, especially meat exceeding the state reserve time several times, has always been a big challenge in food safety inspection. Under long freezing times, the physical and chemical properties of meat change complexly. In this paper, the SS-OCT detection method under a low-frequency electric field is firstly (to our knowledge) applied to the predict storage durations of long-expired frozen meat. The average normalized cross-correlation (ANCC) is put forward as a comprehensive parameter to reflect both the electric–kinetic and optical properties of meat’s biological changes. A monotonically increasing inversion rule between ANCC and the storage duration of frozen meat is found after investigating 3840 pork samples, the frozen storage durations of which were from 1 to 13 months. To verify the correctness and accuracy of our method, nine groups of long-expired frozen pork samples were investigated. The maximum relative error for their storage durations is less than 5.71%, which means that our SS-OCT method under a low-frequency electric field is promising in providing a rapid on-site storage duration detection method without any complicated laboratory pretreatments for food safety inspection.

More than 50 million older people worldwide are suffering from dementia, and this number is estimated to increase to 150 million by 2050. Greater caregiver burdens and financial impacts on the healthcare system are expected as we wait for an effective treatment for dementia. Researchers are constantly exploring new therapies and screening approaches for the early detection of dementia. Artificial intelligence (AI) is widely applied in dementia research, including machine learning and deep learning methods for dementia diagnosis and progression detection. Computerized apps are also convenient tools for patients and caregivers to monitor cognitive function changes. Furthermore, social robots can potentially provide daily life support or guidance for the elderly who live alone. This review aims to provide an overview of AI applications in dementia research. We divided the applications into three categories according to different stages of cognitive impairment: (1) cognitive screening and training, (2) diagnosis and prognosis for dementia, and (3) dementia care and interventions. There are numerous studies on AI applications for dementia research. However, one challenge that remains is comparing the effectiveness of different AI methods in real clinical settings.

ObjectiveThe effect of arginine on tumors appears to be bidirectional. The association of serum arginine with the risk of incident cancer remains uncovered at present. We aimed to investigate the prospective relationship of baseline serum arginine concentrations with the risk of incident cancer in hypertensive participants.Materials and methodsA nested, case-control study with 1,389 incident cancer cases and 1,389 matched controls was conducted using data from the China H-Type Hypertension Registry Study (CHHRS). Conditional logistic regression analyses were performed to evaluate the association between serum arginine and the risk of the overall, digestive system, non-digestive system, and site-specific cancer.ResultsCompared with matched controls, cancer patients had higher levels of arginine (21.41 μg/mL vs. 20.88 μg/mL, p < 0.05). When serum arginine concentrations were assessed as quartiles, compared with participants in the lowest arginine quartile, participants in the highest arginine quartile had a 32% (OR = 1.32, 95% CI: 1.03 to 1.71), and 68% (OR = 1.68, 95% CI: 1.09 to 2.59) increased risk of overall and digestive system cancer, respectively, in the adjusted models. In the site-specific analysis, each standard deviation (SD) increment of serum arginine was independently and positively associated with the risk of colorectal cancer (OR = 1.35, 95% CI: 1.01 to 1.82) in the adjusted analysis.ConclusionWe found that hypertensive individuals with higher serum arginine levels exhibited a higher risk of overall, digestive system, and colorectal cancer.

Abstract Background The senescence of dermal fibroblasts (DFLs) leads to an imbalance in the synthesis and degradation of extracellular matrix (ECM) proteins, presenting so-called senescence-associated secretory phenotype (SASP), which ultimately leads to skin aging. Recently, mesenchymal stem cell (MSC)-derived extracellular vesicles (EVs) have been recognized as a promising cell-free therapy for degenerative diseases, which opens a new avenue for skin aging treatment. Methods In this study, we utilized chitosan (CS) hydrogel for effective loading and sustained release of EVs. In vitro, we explored the rejuvenation effects of CS hydrogel-incorporated EVs (CS-EVs) on replicative senescence DFLs through a series of experiments such as senescence-associated β-galactosidase (SA-β-gal) staining, RT-PCR, and Western blot analysis. Besides, we employed local multi-site subcutaneous injection to treat skin aging of naturally aged mice with CS-EVs and DiI fluorescent dye was used to label EVs to achieve in vivo real-time tracking. Results CS-EVs can significantly improve the biological functions of senescent fibroblasts, including promoting their proliferation, enhancing the synthesis of ECM proteins, and inhibiting the overexpression of matrix metalloproteinases (MMPs). Moreover, CS hydrogel could prolong the release of EVs and significantly increase the retention of EVs in vivo. After CS-EVs subcutaneous injection treatment, the aging skin tissues showed a rejuvenation state, manifested explicitly as the enhanced expression of collagen, the decreased expression of SASP-related factors, and the restoration of tissue structures. Conclusions CS hydrogel-encapsulated EVs could delay the skin aging processes by ameliorating the function of aging DFLs. Our results also highlight the potential of CS hydrogel-encapsulated EVs as a novel therapeutic strategy for improving aging skin to rejuvenation.

Although a number of studies have provided information on soil texture, soil classification, and depth to bedrock throughout China, few studies have combined this information, which is the basis for agricultural field management. A total of 81% of China’s cultivated lands are distributed among the Middle–Lower Yangtze Plain (18.2%), arid and semiarid North China Plain (18.2%), Northeast Plain (17%), Huang-Huai-Hai Plain (16.1%), and Yunnan–Guizhou Plateau (11.6%). The Huang-Huai-Hai Plain has the highest density of agricultural land (58.5%) and the greatest depth to bedrock of cultivated land (243–402 m). The lowest cultivated depth to bedrock (4–84 m) is concentrated in the Sichuan Basin and its surrounding regions. The main cultivated soil types are Anthrosols, Fluvisols, Cambisols, Phaeozems, Luvisols, Kastanozems, Leptosols, and Acrisols, under the main topsoil texture classes of loam, clay loam, silty clay loam, silt loam, sandy loam, and clay. The Fluvisols had the largest depth to bedrock (156 m) on the Middle–Lower Yangtze Plain and Huang-Huai-Hai Plain, with the highest silt soil distributions but comparable lower sand contents. The Yunnan–Guizhou Plateau had the highest clay soil content. The cultivation under Kastanozems and Leptosols on the Qinghai–Tibet Plateau and in arid and semiarid North China and under Phaeozems on the Northeast Plain should be restricted and managed very cautiously facing erosion risk. The higher percentages of Anthrosols are on the Middle–Lower Yangtze Plain (37%), in Southern China (32%), and on the Yunnan–Guizhou Plateau (26%). The same cultivation aim (i.e., more crop 0production) has produced a similar range of properties over time among the soils developed on agricultural fields, which are classified as Anthrosols. However, various soil types can still be found in agroecosystems because of the variations in climate and topography. Our results highlight that the agriculture-based soil climate and topography shape the interaction of the soil development and not only the pedogenic history of the soil development under variations in the soil depth to bedrock but also the cultivation of distinct pedogenic features. This study provides cultivated soil information on the depth to bedrock, soil classification, and soil texture in China, as well as instructions for field strategies for sustainable agricultural development.

Background and AimsMalnutrition is highly prevalent and is related to multiple impaired clinical outcomes in cancer patients. This study aimed to de novo create an objective, nutrition-related index specially for prognostic purposes in oncology populations.MethodsWe performed a multicenter cohort study including 14,134 cancer patients. The prognostic impact for each baseline characteristic was estimated by calculating Harrell's C-index. The optimal parameters reflecting the nutritional and inflammatory impact on patients' overall survival were selected to develop the fat-age-inflammation (FAIN) index. The associations of the FAIN with the nutritional status, physical performance, quality of life, short-term outcomes and mortality of patients were comprehensively evaluated. Independent external validation was performed to further assess the prognostic value of the FAIN.ResultsThe study enrolled 7,468 men and 6,666 women with a median age of 57 years and a median follow-up of 42 months. The FAIN index was defined as: (triceps skinfold thickness + albumin) / [age + 5 × (neutrophil count/lymphocyte count)]. There were significant associations of the FAIN with the nutritional status, physical performance, quality of life and short-term outcomes. The FAIN also showed better discrimination performance than the Nutritional Risk Index, the Prognostic Nutritional Index and the Controlling Nutritional Status index (all P < 0.05). In multivariable-adjusted models, the FAIN was independently associated with a reduced death hazard both as a continuous variable (HR = 0.57, 95%CI = 0.47–0.68) and per one standard deviation (HR = 0.83, 95%CI = 0.78–0.88). External validation in a multicenter lung cancer cohort (n = 227) further confirmed the prognostic value of the FAIN.ConclusionsThis study created and assessed the prognostic FAIN index, which might act as a feasible option to monitor the nutritional status and help develop intervention strategies to optimize the survival outcomes of cancer patients.

Soil salinization is one of the main causes of global desertification and soil degradation. Although previous studies have investigated the hyperspectral inversion of soil salinity using machine learning, only a few have been based on soil types. Moreover, agricultural fields can be improved based on the accurate estimation of the soil salinity, according to the soil type. We collected field data relating to six salinized soils, Haplic Solonchaks (HSK), Stagnic Solonchaks (SSK), Calcic Sonlonchaks (CSK), Fluvic Solonchaks (FSK), Haplic Sonlontzs (HSN), and Takyr Solonetzs (TSN), in the Hetao Plain of the upper reaches of the Yellow River, and measured the in situ hyperspectral, pH, and electrical conductivity (EC) values of a total of 231 soil samples. The two-dimensional spectral index, topographic factors, climate factors, and soil texture were considered. Several models were used for the inversion of the saline soil types: partial least squares regression (PLSR), random forest (RF), extremely randomized trees (ERT), and ridge regression (RR). The spectral curves of the six salinized soil types were similar, but their reflectance sizes were different. The degree of salinization did not change according to the spectral reflectance of the soil types, and the related properties were inconsistent. The Pearson’s correlation coefficient (PCC) between the two-dimensional spectral index and the EC was much greater than that between the reflectance and EC in the original band. In the two-dimensional index, the PCC of the HSK-NDI was the largest (0.97), whereas in the original band, the PCC of the SSK400 nm was the largest (0.70). The two-dimensional spectral index (NDI, RI, and DI) and the characteristic bands were the most selected variables in the six salinized soil types, based on the variable projection importance analysis (VIP). The best inversion model for the HSK and FSK was the RF, whereas the best inversion model for the CSK, SSK, HSN, and TSN was the ERT, and the CSK-ERT had the best performance (R2 = 0.99, RMSE = 0.18, and RPIQ = 6.38). This study provides a reference for distinguishing various salinization types using hyperspectral reflectance and provides a foundation for the accurate monitoring of salinized soil via multispectral remote sensing.

An accurate estimation of soil electrical conductivity (EC) using hyperspectral techniques is of great significance for understanding the spatial distribution of solutes and soil salinization. Although spectral transformation has been widely used in data pre-processing, the performance of different pre-processing techniques (or combination methods) on different models of the same data set is still ambiguous. Moreover, extremely randomized trees (ERT) and light gradient boosting machine (LightGBM) models are new learning algorithms with good generalization performance (soil moisture and above-ground biomass), but are less studied in estimating soil salinity in the visible and near-infrared spectra. In this study, 130 soil EC data, soil measured hyperspectral data, topographic factors, conventional salinity indices such as Salinity Index 1, and two-band (2D) salinity indices such as ratio indices, were introduced. The five spectral pre-processing methods of standard normal variate (SNV), standard normal variate and detrend (SNV-DT), inverse (1/OR) (OR is original spectrum), inverse-log (Log(1/OR) and fractional order derivative (FOD) (range 0–2, with intervals of 0.25) were performed. A gradient boosting machine (GBM) was used to select sensitive spectral parameters. Models (extreme gradient boosting (XGBoost), LightGBM, random forest (RF), ERT, classification and regression tree (CART), and ridge regression (RR)) were used for inversion soil EC and model validation. The results reveal that the two-dimensional correlation coefficient highlighted EC more effectively than the one-dimensional. Under SNV and the second order derivative, the two-dimensional correlation coefficient increased by 0.286 and 0.258 compared to the one-dimension, respectively. The 13 characteristic factors of slope, NDI, SI-T, RI, profile curvature, DOA, plane curvature, SI (conventional), elevation, Int2, aspect, S1 and TWI provided 90% of the cumulative importance for EC using GBM. Among the six machine models, the ERT model performed the best for simulation (R2 = 0.98) and validation (R2 = 0.96). The ERT model showed the best performance among the EC estimation models from the reference data. The kriging map based on the ERT simulation showed a close relationship with the measured data. Our study selected the effective pre-processing methods (SNV and the 2 order derivative) using one- and two-dimensional correlation, 13 important factors and the ERT model for EC hyperspectral inversion. This provides a theoretical support for the quantitative monitoring of soil salinization on a larger scale using remote sensing techniques.

The telomeric CTC1/STN1/TEN1 (CST) complex has been implicated in promoting replication recovery under replication stress at genomic regions, yet its precise role is unclear. Here, we report that STN1 is enriched at GC-rich repetitive sequences genome-wide in response to hydroxyurea (HU)-induced replication stress. STN1 deficiency exacerbates the fragility of these sequences under replication stress, resulting in chromosome fragmentation. We find that upon fork stalling, CST proteins form distinct nuclear foci that colocalize with RAD51. Furthermore, replication stress induces physical association of CST with RAD51 in an ATR-dependent manner. Strikingly, CST deficiency diminishes HU-induced RAD51 foci formation and reduces RAD51 recruitment to telomeres and non-telomeric GC-rich fragile sequences. Collectively, our findings establish that CST promotes RAD51 recruitment to GC-rich repetitive sequences in response to replication stress to facilitate replication restart, thereby providing insights into the mechanism underlying genome stability maintenance.

A high-speed, high-resolution swept-source optical coherence tomography (SS-OCT) is presented for focusing lens imaging and a k-domain uniform algorithm is adopted to find the wave number phase equalization. The radius of curvature of the laser focusing lens was obtained using a curve-fitting algorithm. The experimental results demonstrate that the measuring accuracy of the proposed SS-OCT system is higher than the laser confocal microscope. The SS-OCT system has great potential for surface topography measurement and defect inspection of the focusing lens.

The incorporation of human APOBEC3G (hA3G) into HIV is required for exerting its antiviral activity, therefore the mechanism underlying hA3G virion encapsidation has been investigated extensively. hA3G was shown to form low-molecular-mass (LMM) and high-molecular-mass (HMM) complexes. The function of different forms of hA3G in its viral incorporation remains unclear.In this study, we investigated the subcellular distribution and lipid raft association of hA3G using subcellular fractionation, membrane floatation assay and pulse-chase radiolabeling experiments respectively, and studied the correlation between the ability of hA3G to form the different complex and its viral incorporation. Our work herein provides evidence that the majority of newly-synthesized hA3G interacts with membrane lipid raft domains to form Lipid raft-associated hA3G (RA hA3G), which serve as the precursor of mature HMM hA3G complex, while a minority of newly-synthesized hA3G remains in the cytoplasm as a soluble LMM form. The distribution of hA3G among the soluble LMM form, the RA LMM form and the mature forms of HMM is regulated by a mechanism involving the N-terminal part of the linker region and the C-terminus of hA3G. Mutagenesis studies reveal a direct correlation between the ability of hA3G to form the RA LMM complex and its viral incorporation.Together these data suggest that the Lipid raft-associated LMM A3G complex functions as the cellular source of viral hA3G.

Abstract The performance and operational stability of perovskite‐based devices heavily rely on the interfacial properties between the photoactive perovskite layer and charge transport layers. Understanding the theoretical relationship between surface/interface dipoles and surface energetics is crucial for scientific understanding and practical applications. In this study, a method is applied that bridges classical electromagnetism and modern atomistic approaches. The impact of dipolar ligand molecules functionalizing the FASnI3 perovskite surface is investigated, with inspection of the interplay between surface dipole, charge transfer, and local strain effect, and corresponding shifts in the valence level. The results reveal that the contribution of individual molecular entities to surface dipoles and electric susceptibilities follows an essentially additive behavior. The influence of F doping usually used to mitigate Sn oxidation in Sn‐based layer for photovoltaic applications, is also discussed. Furthermore, the findings are compared with predictions from classical approaches employing a capacitor model that links the induced vacuum level shift and the molecular dipole moment. The insights gained from the study provide theoretical guidelines for fine‐tuning the work functions of materials, thus enabling effective interfacial engineering in this class of semiconductors.

Abstract Background Remimazolam is a novel ultrashort-acting intravenous benzodiazepine sedative-hypnotic. The combination of remimazolam and sevoflurane does not increase respiratory sensitivity, produce bronchospasm, or cause other adverse conditions. We aimed to observe the effects of different remimazolam doses on the minimum alveolar concentration (MAC) of sevoflurane at end-expiration during laryngeal mask insertion and evaluate the effect of sex on the efficacy of the combination of remimazolam on the suppression of laryngeal mask insertion in adult patients. Methods We included 240 patients undergoing laparoscopic surgery under general anesthesia with elective placement of a laryngeal mask (120 males and 120 females). The patients were randomly divided into four groups according to sex: a control group (randomization for female patients, RF0; randomization for male patients, RM0) and three remimazolam groups (RF1, RM1 / RM2, RF2 / RM3, RF3), with 30 patients in each group. Induction was established by vital capacity rapid inhalation induction (VCRII), using 8% sevoflurane and 100% oxygen (6 L/min) in all patients. The (RF1, RM1), (RM2, RF2), and (RM3, RF3) groups were continuously injected with remimazolam at doses of 1, 1.5, and 2.0 mg/kg/h, respectively, while the (RM0, RF0) group was injected with an equal volume of normal saline. The end-expiratory concentration of sevoflurane was adjusted to a preset value after the patient’s eyelash reflex disappeared. After the end-expiratory concentration of sevoflurane was kept stable for at least 15 min, the laryngeal mask was placed, and the patient’s physical response to the mask placement was observed immediately and within 30 s of placement. The MAC of sevoflurane was measured using the up-and-down sequential method of Dixon. Results The calculated MAC of end-expiratory sevoflurane during laryngeal mask insertion in adult females was (2.94 ± 0.18)%, (2.69 ± 0.16)%, (2.32 ± 0.16)% and (1.83 ± 0.15)% in groups RF0, RF1, RF2 and RF3; (2.98 ± 0.18)%, (2.80 ± 0.19)%, (2.54 ± 0.15)% and (2.15 ± 0.15)% in male groups RM0, RM1, RM2 and RM3, respectively. The MAC values were significantly lower in the (RF1-RF3, RM1-RM3) group when compared to the (RF0, RM0) group. There was no significant difference between (RF0, RF1) and (RM0, RM1), but the MAC value of the RF2-RF3 group was significantly lower than that of the RM2-RM3 group. Conclusions Remimazolam can effectively reduce end-expiratory sevoflurane MAC values during laryngeal mask placement in adults. When remimazolam was measured above 1.5 mg/kg/h, the effect of inhibiting laryngeal mask implantation in female patients was stronger than that in male patients. Remimazolam at a dose of 1–2 mg/kg/h combined with sevoflurane induction can be safely and effectively used in these patients.

More than 50 million older people worldwide are suffering from dementia, and this number is estimated to increase to 150 million by 2050. Greater caregiver burdens and financial impacts on the healthcare system are expected as we wait for an effective treatment for dementia. Researchers are constantly exploring new therapies and screening approaches for the early detection of dementia. Artificial intelligence (AI) is widely applied in dementia research, including machine learning and deep learning methods for dementia diagnosis and progression detection. Computerized apps are also convenient tools for patients and caregivers to monitor cognitive function changes. Furthermore, social robots can potentially provide daily life support or guidance for the elderly who live alone. This review aims to provide an overview of AI applications in dementia research. We divided the applications into three categories according to different stages of cognitive impairment: (1) cognitive screening and training, (2) diagnosis and prognosis for dementia, and (3) dementia care and interventions. There are numerous studies on AI applications for dementia research. However, one challenge that remains is comparing the effectiveness of different AI methods in real clinical settings. [ABSTRACT FROM AUTHOR]

Piezoresistive pressure sensors have broad applications but often face accuracy challenges due to temperature-induced drift. Traditional compensation methods based on discrete data, such as polynomial interpolation, support vector machine (SVM), and artificial neural network (ANN), overlook the thermal hysteresis, resulting in lower accuracy. Considering the sequence-dependent nature of temperature drift, we propose the RF-IWOA-GRU temperature compensation model. Random forest (RF) is used to interpolate missing values in continuous data. A combination of gated recurrent unit (GRU) networks and an improved whale optimization algorithm (IWOA) is employed for temperature compensation. This model leverages the memory capability of GRU and the optimization efficiency of the IWOA to enhance the accuracy and stability of the pressure sensors. To validate the compensation method, experiments were designed under continuous variations in temperature and actual pressure. The experimental results show that the compensation capability of the proposed RF-IWOA-GRU model significantly outperforms that of traditional methods. After compensation, the standard deviation of pressure decreased from 10.18 kPa to 1.14 kPa, and the mean absolute error and root mean squared error were reduced by 75.10% and 76.15%, respectively.

Abstract Background Erector spinae plane block (ESPB) improves postoperative analgesia and significantly enhances the quality of recovery (QoR) after video-assisted thoracoscopic lobectomy surgery (VATLS). However, it is not known whether the use of dexmedetomidine (Dex) as an adjunct for ropivacaine to ESPB affects the QoR after VATLS. The purpose of this study was to explore the effects of different Dex dosages as an adjunct for ropivacaine in combination with ultrasound-guided ESPB on the quality of postoperative recovery in patients with VATLS. Methods In this single-center, double-blind, randomized study, 120 patients between the ages of 18 and 65 who were scheduled for VATLS from december 2021 and october 2022 in our hospital under general anesthesia were randomly divided into three groups: ultrasound-guided ESPB with 30 mL of 0.5% ropivacaine (Group R), ultrasound-guided ESPB 0.5% ropivacaine plus 0.5 µg/kg Dex (Group RD1), and ultrasound-guided ESPB 0.5% ropivacaine plus 1.0 µg/kg Dex (Group RD2), ultrasound-guided ESPB was administrated at the T5 vertebral level before surgery. The primary outcome was the QoR-15 score 24 h after the surgery. The secondary outcomes included the QoR-15 scores at 12 h, 48 h, and 72 h after the operation, visual analogue scale (VAS) scores at 8 h, 12 h, 24 h, and 48 h after surgery, cumulative flurbiprofen consumption, postoperative nausea and vomiting (PONV), postoperative bradycardia, and hypotension. Results The QoR-15 scores were higher in group RD2 than the R and RD1 groups on postoperative day 1 (P

Stable isotopes in natural water bodies serve as hydrologic tracers, with a history of extensive application in karst groundwater research. The present paper is a systematic review of previous research results, with the objective of sorting out and summarizing the stable isotopic characteristics of karst groundwater in southwest China, which is the most typical karst region worldwide. Comprehensive analysis reveals that the deuterium and oxygen isotope values of most karst groundwater are characterized temporally as ‘enriched in the rainy season and depleted in the dry season’, which is the opposite of the pattern of precipitation. While the spatial distribution feature is basically consistent with the spatial variation pattern of precipitation, which is characterized as ‘depleted from the coast to the interior’. Additionally, the main applications of stable isotopes to karst groundwater are discussed, including karst groundwater recharge source and recharge elevation identification, research on karst groundwater in the hydrological cycle, the hydrological process of karst groundwater, and karst groundwater contamination tracking. Finally, we looked forward to future research on karst groundwater based on deuterium and oxygen isotopes. It is our hope that this review may provide insight into the study of karst groundwater.

Multimodal perception is the predominant means by which individuals experience and interact with the world. However, sensory dysfunction or loss can significantly impede this process. In such cases, cross-modality research offers valuable insight into how we can compensate for these sensory deficits through sensory substitution. Although sight and hearing are both used to estimate the distance to an object (e.g., by visual size and sound volume) and the perception of distance is an important element in navigation and guidance, it is not widely studied in cross-modal research. We investigate the relationship between audio and vibrotactile frequencies (in the ranges 47-2,764 Hz and 10-99 Hz, respectively) and distances uniformly distributed in the range 1-12 m. In our experiments participants mapped the distance (represented by an image of a model at that distance) to a frequency via adjusting a virtual tuning knob. The results revealed that the majority (more than 76%) of participants demonstrated a strong negative monotonic relationship between frequency and distance, across both vibrotactile (represented by a natural log function) and auditory domains (represented by an exponential function). However, a subgroup of participants showed the opposite positive linear relationship between frequency and distance. The strong cross-modal sensory correlation could contribute to the development of assistive robotic technologies and devices to augment human perception. This work provides the fundamental foundation for future assisted HRI applications where a mapping between distance and frequency is needed, for example for people with vision or hearing loss, drivers with loss of focus or response delay, doctors undertaking teleoperation surgery, and users in augmented reality (AR) or virtual reality (VR) environments.

ABSTRACTNon-invasive Micro-test Technology (NMT) measures the ions/molecules in and out of the samples in a real-time, three-dimensional and dynamic manner without damaging the biological samples to obtain ions/molecules concentration, flow rate and movement direction, providing evidence for the study of the transport mechanism of biological and external environmental substances. This paper introduces the structure, operation system and technical advantages of NMT, and compares it with other ion testing technologies. Additionally, we conclude that it possesses the following attributes: no damage to biological activity, large range of measurable samples and targets, high sensitivity, and rich data content. Furthermore, its application progress in recent years has been reviewed and prospected from the perspectives of environmental bioremediation and stress physiology, which provides a reference for sustainable biological and environmental research.