Long-term exposure to major constituents of fine particulate matter and neurodegenerative diseases: A population-based survey in the Pearl River Delta Region, China.

BACKGROUND: Exposure to PM2.5 has been linked to neurodegenerative diseases, with limited understanding of constituent-specific contributions. OBJECTIVES: To explore the associations between long-term exposure to PM2.5 constituents and neurodegenerative diseases. METHODS: We recruited 148,274 individuals aged ≥ 60 from four cities in the Pearl River Delta region, China (2020 to 2021). We calculated twenty-year average air pollutant concentrations (PM2.5 mass, black carbon (BC), organic matter (OM), ammonium (NH4+), nitrate (NO3-) and sulfate (SO42-)) at the individuals' home addresses. Neurodegenerative diseases were determined by self-reported doctor-diagnosed Alzheimer's disease (AD) and Parkinson's disease (PD). Generalized linear mixed models were employed to explore associations between pollutants and neurodegenerative disease prevalence. RESULTS: PM2.5 and all five constituents were significantly associated with a higher prevalence of AD and PD. The observed associations generally exhibited a non-linear pattern. For example, compared with the lowest quartile, higher quartiles of BC were associated with greater odds for AD prevalence (i.e., the adjusted odds ratios were 1.81; 95% CI, 1.45-2.27; 1.78; 95% CI, 1.37-2.32; and 1.99; 95% CI, 1.54-2.57 for the second, third, and fourth quartiles, respectively). CONCLUSIONS: Long-term exposure to PM2.5 and its constituents, particularly combustion-related BC, OM, and SO42-, was significantly associated with higher prevalence of AD and PD in Chinese individuals. ENVIRONMENTAL IMPLICATION: PM2.5 is a routinely regulated mixture of multiple hazardous constituents that can lead to diverse adverse health outcomes. However, current evidence on the specific contributions of PM2.5 constituents to health effects is scarce. This study firstly investigated the association between PM2.5 constituents and neurodegenerative diseases in the moderately to highly polluted Pearl River Delta region in China, and identified hazardous constituents within PM2.5 that have significant impacts. This study provides important implications for the development of targeted PM2.5 prevention and control policies to reduce specific hazardous PM2.5 constituents.

A prognostic model for triple-negative breast cancer patients with liver metastasis: A population-based study.

However, it is still difficult for clinicians to establish prognostic stratifications and therapeutic strategies because of the lack of tools for predicting the survival of triple-negative breast cancer patients with liver metastases (TNBC-LM). Based on clinical data from large populations, a sensitive and discriminative nomogram was developed and validated to predict the prognosis of TNBC patients with LM at initial diagnosis or at the later course. Introduction/background: Liver metastasis (LM) in TNBC patients is associated with significant morbidity and mortality. The objective of this study was to construct a clinical model to predict the survival of TNBC-LM patients. Materials and methods: Clinicopathologic data were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and the Fifth Affiliated Hospital of Sun Yat-Sen University (FAFSYU). Based on patients with newly diagnosed TNBC with LM (nTNBC-LM) from the SEER database, a predictive nomogram was established and validated. Its predictive effect on TNBC patients with LM at later disease course by enrolling TNBC patients from FAFSYU who developed LM later. The prognostic effect of different treatment for nTNBC-LM was further assessed. Results: A prognostic model was developed and validated to predict the prognosis of TNBC-LM patients. For LM patients diagnosed at the initial or later treatment stage, the C-index (0.712, 0.803 and 0.699 in the training, validation and extended groups, respectively) and calibration plots showed the acceptable prognostic accuracy and clinical applicability of the nomogram. Surgical resection on the primary tumour and chemotherapy were found to be associated with significantly better overall survival (OS). Conclusion: A sensitive and discriminative model was developed to predict OS in TNBC-LM patients both at and after initial diagnosis.

Lipids and lipid metabolism in cellular senescence: Emerging targets for age-related diseases.

Cellular senescence is a kind of cellular state triggered by endogenous or exogenous stimuli, which is mainly characterized by stable cell cycle arrest and complex senescence-associated secretory phenotype (SASP). Once senescent cells accumulate in tissues, they may eventually accelerate the progression of age-related diseases, such as atherosclerosis, osteoarthritis, chronic lung diseases, cancers, etc. Recent studies have shown that the disorders of lipid metabolism are not only related to age-related diseases, but also regulate the cellular senescence process. Based on existing research evidences, the changes in lipid metabolism in senescent cells are mainly concentrated in the metabolic processes of phospholipids, fatty acids and cholesterol. Obviously, the changes in lipid-metabolizing enzymes and proteins involved in these pathways play a critical role in senescence. However, the link between cellular senescence, changes in lipid metabolism and age-related disease remains to be elucidated. Herein, we summarize the lipid metabolism changes in senescent cells, especially the senescent cells that promote age-related diseases, as well as focusing on the role of lipid-related enzymes or proteins in senescence. Finally, we explore the prospect of lipids in cellular senescence and their potential as drug targets for preventing and delaying age-related diseases.

Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices.

BACKGROUND: Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms. METHODS: This study included 92 participants with variable degrees of KOA. A modified Kellgren-Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost). RESULTS: Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability. CONCLUSION: Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans.

Systemic immune-inflammation index and all-cause and cause-specific mortality in sarcopenia: a study from National Health and Nutrition Examination Survey 1999-2018.

Background: Sarcopenia, common in the elderly, often linked to chronic diseases, correlates with inflammation.The association between SII and mortality in sarcopenia patients is underexplored, this study investigates this relationship in a U.S. adult cohort. Methods: We analyzed 1999-2018 NHANES data, focusing on 2,974 adults with sarcopenia. Mortality outcomes were determined by linking to National Death Index (NDI) records up to December 31, 2019. Using a weighted sampling design, participants were grouped into three groups by the Systemic Immune-Inflammation Index (SII). We used Cox regression models, adjusting for demographic and clinical variables, to explore SII's association with all-cause and cause-specific mortality in sarcopenia, performing sensitivity analyses for robustness. Results: Over a median follow-up of 9.2 years, 829 deaths occurred. Kaplan-Meier analysis showed significant survival differences across SII groups. The highest SII group showed higher hazard ratios (HRs) for all-cause and cause-specific mortality in both crude and adjusted models. The highest SII group had a higher HR for all-cause(1.57, 1.25-1.98), cardiovascular(1.61, 1.00-2.58), cancer(2.13, 1.32-3.44), and respiratory disease mortality(3.21, 1.66-6.19) in fully adjusted models. Subgroup analyses revealed SII's association with all-cause mortality across various demographics, including age, gender, and presence of diabetes or cardiovascular disease. Sensitivity analyses, excluding participants with cardiovascular diseases, those who died within two years of follow-up, or those under 45 years of age, largely reflected these results, with the highest SII group consistently demonstrating higher HRs for all types of mortality in both unadjusted and adjusted models. Conclusion: Our study is the first to demonstrate a significant relationship between SII and increased mortality risks in a sarcopenia population.

Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle.

As an essential reference to bridge dynamic characteristics, the identification of bridge frequencies has far-reaching consequences for the health monitoring and damage evaluation of bridges. This study proposes a uniform scheme to identify bridge frequencies with two different subspace-based methodologies, i.e., an improved Short-Time Stochastic Subspace Identification (ST-SSI) method and an improved Multivariable Output Error State Space (MOESP) method, by simply adjusting the signal inputs. One of the key features of the proposed scheme is the dimensionless description of the vehicle-bridge interaction system and the employment of the dimensionless response of a two-axle vehicle as the state input, which enhances the robustness of the vehicle properties and speed. Additionally, it establishes the equation of the vehicle biaxial response difference considering the time shift between the front and the rear wheels, theoretically eliminating the road roughness information in the state equation and output signal effectively. The numerical examples discuss the effects of vehicle speeds, road roughness conditions, and ongoing traffic on the bridge identification. According to the dimensionless speed parameter Sv1 of the vehicle, the ST-SSI (Sv1 < 0.1) or MOESP (Sv1 ≥ 0.1) algorithm is applied to extract the frequencies of a simply supported bridge from the dimensionless response of a two-axle vehicle on a single passage. In addition, the proposed methodology is applied to two types of long-span complex bridges. The results show that the proposed approaches exhibit good performance in identifying multi-order frequencies of the bridges, even considering high vehicle speeds, high levels of road surface roughness, and random traffic flows.

LiverRisk score: An accurate, cost-effective tool to predict fibrosis, liver-related, and diabetes-related mortality in the general population.

BACKGROUND: Noninvasive and early assessment of liver fibrosis is of great significance and is challenging. We aimed to evaluate the predictive performance and cost-effectiveness of the LiverRisk score for liver fibrosis and liver-related and diabetes-related mortality in the general population. METHODS: The general population from the NHANES 2017-March 2020, NHANES 1999-2018, and UK Biobank 2006-2010 were included in the cross-sectional cohort (n = 3,770), along with the NHANES follow-up cohort (n = 25,317) and the UK Biobank follow-up cohort (n = 17,259). The cost-effectiveness analysis was performed using TreeAge Pro software. Liver stiffness measurements ≥10 kPa were defined as compensated advanced chronic liver disease (cACLD). FINDINGS: Compared to conventional scores, the LiverRisk score had significantly better accuracy and calibration in predicting liver fibrosis, with an area under the receiver operating characteristic curve (AUC) of 0.76 (0.72-0.79) for cACLD. According to the updated thresholds of LiverRisk score (6 and 10), we reclassified the population into three groups: low, medium, and high risk. The AUCs of LiverRisk score for predicting liver-related and diabetes-related mortality at 5, 10, and 15 years were all above 0.8, with better performance than the Fibrosis-4 score. Furthermore, compared to the low-risk group, the medium-risk and high-risk groups in the two follow-up cohorts had a significantly higher risk of liver-related and diabetes-related mortality. Finally, the cost-effectiveness analysis showed that the incremental cost-effectiveness ratio for LiverRisk score compared to FIB-4 was USD $18,170 per additional quality-adjusted life-year (QALY) gained, below the willingness-to-pay threshold of $50,000/QALY. CONCLUSIONS: The LiverRisk score is an accurate, cost-effective tool to predict liver fibrosis and liver-related and diabetes-related mortality in the general population. FUNDING: The National Natural Science Foundation of China (nos. 82330060, 92059202, and 92359304); the Key Research and Development Program of Jiangsu Province (BE2023767a); the Fundamental Research Fund of Southeast University (3290002303A2); Changjiang Scholars Talent Cultivation Project of Zhongda Hospital of Southeast University (2023YJXYYRCPY03); and the Research Personnel Cultivation Program of Zhongda Hospital Southeast University (CZXM-GSP-RC125).

New Method for 5'-Nucleotidase Preparation and Evaluation of Its Catalytic Activity.

In this study, we established a new methodology for preparing 5'-nucleotidase (5'-NT) with the aim of enhancing our understanding of its enzyme activity and laying a basis for regulating the content of umami-enhancing nucleotides in pork. 5'-NT was prepared with Sephadex gel filtration and reverse-phase high-performance liquid chromatography, and its enzymatic properties and catalytic activity were evaluated. The results show that the molecular weight of the prepared 5'-NT was 57 kDa, the optimal catalytic temperature was 40 °C, and the optimal pH was 8. Zn2+, and sucrose showed inhibitory effects on the activity of 5'-NT, while K+, Na+, Ca2+, Mg2+, glucose, fructose, and trehalose promoted the activity of the studied compound. The prepared 5'-NT exhibited higher catalytic activity and selectivity against IMP compared with its commercial counterpart, while its catalytic activity against XMP was not significant (p > 0.05). In brief, we established a new methodology for preparing 5'-NT, enhancing our understanding of its enzyme activity and providing a solid basis for regulating the content of umami-enhancing nucleotides in pork through the control of endogenous 5'-NT activity.

Palladium-Catalyzed Directed Carbon-Carbon Bond Activation of Aryl Nitriles for Cyano Transfer.

Herein, we report the C-H cyanation of indoles via a palladium-catalyzed directed C-CN activation reaction using aryl nitrile as a cyano source. The employment of the phenoxy-oriented group is the key to the cleavage of the C-CN bond. This protocol features a broad substrate scope, good efficiency, and high regioselectivity. Furthermore, the practical application of this protocol was showcased in the late-stage functionalization and synthesis of indole derivatives, which were derived from drugs and natural products, through the process of cyanation.

Metal-organic cages for gas adsorption and separation.

The unique high surface area and tunable cavity size endow metal-organic cages (MOCs) with superior performance and broad application in gas adsorption and separation. Over the past three decades, for instance, numerous MOCs have been widely explored in adsorbing diverse types of gas including energy gases, greenhouse gases, toxic gases, noble gases, etc. To gain a better understanding of the structure-performance relationships, great endeavors have been devoted to ligand design, metal node regulation, active metal site construction, cavity size adjustment, and function-oriented ligand modification, thus opening up routes toward rationally designed MOCs with enhanced capabilities. Focusing on the unveiled structure-performance relationships of MOCs towards target gas molecules, this review consists of two parts, gas adsorption and gas separation, which are discussed separately. Each part discusses the cage assembly process, gas adsorption strategies, host-guest chemistry, and adsorption properties. Finally, we briefly overviewed the challenges and future directions in the rational development of MOC-based sorbents for application in challenging gas adsorption and separation, including the development of high adsorption capacity MOCs oriented by adsorbability and the development of highly selective adsorption MOCs oriented by separation performance.

Effect of destability time on microstructure and properties of hypoeutectic high chromium cast iron.

The present work investigated the effect of destabilization time on the mechanical properties and microstructure evolution of high chromium cast iron, and scanning electron microscopy and electron probe microanalysis techniques were employed. The results show that the hardness of hypoeutectic high chromium cast iron is related to the size and volume fraction of secondary carbides precipitated from the matrix. The hardness of the alloy continues to rise due to the continuous increase of the volume fraction of the secondary carbide at the initial stage of destabilization. The alloy reaches its peak hardness value at 950 °C and 1000 °C for 1 hour holding time. The solid solubility of carbon and alloying elements in the matrix increases as the holding time extends, resulting in a large number of carbides redissolved into the matrix, making the hardness of the alloy decrease; the hardness of the alloy at 14 h is less than that at 10 min. Under 1050 °C, the size and density of the secondary carbide increase significantly; extending the holding time will lead to the continuous reduction of the carbide rod that provides strength, thus, the hardness curve shows a downward trend.

Neurological manifestations and risk factors associated with poor prognosis in hospitalized children with Omicron variant infection.

There are increasing reports of neurological manifestation in children with coronavirus disease 2019 (COVID-19). However, the frequency and clinical outcomes of in hospitalized children infected with the Omicron variant are unknown. The aim of this study was to describe the clinical characteristics, neurological manifestations, and risk factor associated with poor prognosis of hospitalized children suffering from COVID-19 due to the Omicron variant. Participants included children older than 28 days and younger than 18 years. Patients were recruited from December 10, 2022 through January 5, 2023. They were followed up for 30 days. A total of 509 pediatric patients hospitalized with the Omicron variant infection were recruited into the study. Among them, 167 (32.81%) patients had neurological manifestations. The most common manifestations were febrile convulsions (n = 90, 53.89%), viral encephalitis (n = 34, 20.36%), epilepsy (n = 23, 13.77%), hypoxic-ischemic encephalopathy (n = 9, 5.39%), and acute necrotizing encephalopathy (n = 6, 3.59%). At discharge, 92.81% of patients had a good prognosis according to the Glasgow Outcome Scale (scores ≥ 4). However, 7.19% had a poor prognosis. Eight patients died during the follow-up period with a cumulative 30-day mortality rate of 4.8% (95% confidence interval (CI) 1.5-8.1). Multivariate analysis revealed that albumin (odds ratio 0.711, 95% CI 0.556-0.910) and creatine kinase MB (CK-MB) levels (odds ratio 1.033, 95% CI 1.004-1.063) were independent risk factors of poor prognosis due to neurological manifestations. The area under the curve for the prediction of poor prognosis with albumin and CK-MB was 0.915 (95%CI 0.799-1.000), indicating that these factors can accurately predict a poor prognosis.          Conclusion: In this study, 32.8% of hospitalized children suffering from COVID-19 due to the Omicron variant infection experienced neurological manifestations. Baseline albumin and CK-MB levels could accurately predict poor prognosis in this patient population. What is Known: • Neurological injury has been reported in SARS-CoV-2 infection; compared with other strains, the Omicron strain is more likely to cause neurological manifestations in adults. • Neurologic injury in adults such as cerebral hemorrhage and epilepsy has been reported in patients with Omicron variant infection. What is New: • One-third hospitalized children with Omicron infection experience neurological manifestations, including central nervous system manifestations and peripheral nervous system manifestations. • Albumin and CK-MB combined can accurately predict poor prognosis (AUC 0.915), and the 30-day mortality rate of children with Omicron variant infection and neurological manifestations was 4.8%.

Integrated neutrophil-to-lymphocyte ratio and handgrip strength better predict survival in patients with cancer cachexia.

OBJECTIVES: Systemic inflammation and skeletal muscle strength play crucial roles in the development and progression of cancer cachexia. In this study we aimed to evaluate the combined prognostic value of neutrophil-to-lymphocyte ratio (NLR) and handgrip strength (HGS) for survival in patients with cancer cachexia. METHODS: This multicenter cohort study involved 1826 patients with cancer cachexia. The NLR-HGS (NH) index was defined as the ratio of neutrophil-to-lymphocyte ratio to handgrip strength. Harrell's C index and receiver operating characteristic (ROC) curve analysis were used to assess the prognosis of NH. Kaplan-Meier analysis and Cox regression models were used to evaluate the association of NH with all-cause mortality. RESULTS: Based on the optimal stratification, 380 women (NH > 0.14) and 249 men (NH > 0.19) were classified as having high NH. NH has shown greater predictive value compared to other indicators in predicting the survival of patients with cancer cachexia according to the 1-, 3-, and 5-y ROC analysis and Harrell's C index calculation. Multivariate survival analysis showed that higher NH was independently associated with an increased risk of death (hazard ratio = 1.654, 95% confidence interval = 1.389-1.969). CONCLUSION: This study demonstrates that the NH index, in combination with NLR and HGS, is an effective predictor of the prognosis of patients with cancer cachexia. It can offer effective prognosis stratification and guidance for their treatment.

A Full-Spectrum ZnS Photocatalyst with Gradient Distribution of Atomic Copper Dopants and Concomitant Sulfur Vacancies for Highly Efficient Hydrogen Evolution.

A rarely discussed phenomenon in the realm of photocatalytic materials involves the presence of gradient distributed dopants and defects from the interior to the surface. This intriguing characteristic has been successfully achieved in the case of ZnS through the incorporation of atomic monovalent copper ions (Cu+) and concurrent sulfur vacancies (Vs), resulting in a photocatalyst denoted as G-CZS1-x. Through the cooperative action of these atomic Cu dopants and Vs, G-CZS1-x significantly extends its photoabsorption range to encompass the full spectrum (200-2100 nm), which improves the solar utilization ability. This alteration enhances the efficiency of charge separation and optimizes Δ(H*) (free energy of hydrogen adsorption) to approach 0 eV for the hydrogen evolution reaction (HER). It is noteworthy that both surface-exposed atomic Cu and Vs act as active sites for photocatalysis. G-CZS1-x exhibits a significant H2 evolution rate of 1.01 mmol h-1 in the absence of a cocatalyst. This performance exceeds the majority of previously reported photocatalysts, exhibiting approximately 25-fold as ZnS, and 5-fold as H-CZS1-x with homogeneous distribution of equal content Cu dopants and Vs. In contrast to G-CZS1-x, the H adsorption on Cu sites for H-CZS1-x (ΔG(H*) = -1.22 eV) is excessively strong to inhibit the H2 release, and the charge separation efficiency for H-CZS1-x is relatively sluggish, revealing the positive role of a gradient distribution model of dopants and defects on activity enhancement. This work highlights the synergy of atomic dopants and defects in advancing photoactivity, as well as the significant benefit of the controllable distribution model of dopants and defects for photocatalysis.

BMSC-Exosomes attenuate ALP dysfunction by restoring lysosomal function via the mTOR/TFEB Axis to reduce cerebral ischemia-reperfusion injury.

BACKGROUND: The complex pathophysiological changes following cerebral ischemia-reperfusion injury (CIRI) include the accumulation of defective proteins and damaged organelles, which cause massive neuron demise. To preserve cellular homeostasis, the autophagy-lysosomal pathway (ALP) is crucial for neurons to dispose of these substances. Many studies have shown that bone mesenchymal stem cell exosomes (BMSC-Exos) can reduce CIRI. However, the specific mechanisms have not been well elucidated, a fact that limits its widespread clinical use. This study aimed to clarify whether BMSC-Exos could attenuate ALP dysfunction by restoring lysosomal function after CIRI via inhibiting mTOR and then activating TFEB nucleus translocation. METHODS: In this study, Flow cytometry, Nanoparticle tracking analysis (NTA), Transmission electron microscope (TEM), and Western blot were used to identify the BMSCs and BMSC-Exos used in this experiment as conforming to the requirements. In vivo experiments, SD rats were modeled with temporary middle cerebral artery occlusion (tMCAO), and BMSC-Exos was injected into the tail vein 2 h after modeling. Triphenyl tetrazolium chloride (TTC) staining, modified neurological severity scores (mNSS), corner turn test, and rotating rod test were used to detect neurological deficits in rats after BMSC-Exos intervention. Western blot and Immunofluorescence were used to detect ALP, transcription factor EB(TFEB) nucleus translocation, and mammalian target of rapamycin (mTOR) change at different time points after modeling and after BMSC-Exos intervention. In vitro experiments, pheochromocytoma cells (PC12) cells were subjected to oxygen-glucose deprivation and reperfusion (OGD/R) modeling to mimic CIRI, and were respectively intervened with BMSC-Exos, BMSC-Exos + MHY 1485 (the mTOR agonist), Rapamycin (the mTOR inhibitor). CCK8, Western blot, and Immunofluorescence were used to detect PC12 cell survival, TFEB nucleus translocation, and cathepsin B(CTSB) Immunofluorescence intensity. RESULTS: We found that ALP dysfunction occurred 72 h after tMCAO, and BMSC-Exos can attenuate ALP dysfunction by restoring lysosomal function. Next, we examined TFEB nucleus translocation and the expression of mTOR, a key regulator of translocation. We found that BMSC-Exos could inhibit mTOR and activate TFEB nucleus translocation. Additional in vitro tests revealed that BMSC-Exos could increase PC12 cell survival after OGD/R, activating TFEB nucleus translocation and enhancing the fluorescence intensity of CTSB, which in turn could be reversed by the mTOR agonist, MHY1485. This effect was similar to another mTOR inhibitor, Rapamycin. CONCLUSION: BMSC-Exos could attenuate ALP dysfunction by restoring lysosomal function after CIRI by inhibiting mTOR and then promoting TFEB nucleus translocation.

High-resolution mapping of age- and gender-specific risk of Clonorchis sinensis infection risk in Guangdong, China: a geostatistical modeling study.

BACKGROUND: The latest national survey on the distribution of human parasites in China demonstrated that Guangdong was among the endemic provinces with the highest Clonorchis sinensis infection rates. High-resolution, age- and gender-specific risk maps of the temporal and spatial distributions are essential for the targeted control work. METHODS: Disease data on the prevalence of C. sinensis infection from 1990 onwards, either age- and gender-specific or aggregated across age and gender, were collected through systematic review and four large-scale surveys in Guangdong Province. Environmental and socioeconomic variables were obtained from open-access databases and employed as potential predictors. A Bayesian geostatistical model was developed to estimate the C. sinensis infection risk at high spatial resolution. RESULTS: The final dataset included 606 surveys at 463 unique locations for C. sinensis infection. Our findings suggested that following an initial increase and stabilization, the overall population-adjusted prevalence had declined to 2.2% (95% Bayesian credible interval: 1.7-3.0%) in the period of 2015 onwards. From 2015 onwards, moderate and high infection risks were found in the northern regions (e.g. Heyuan and Shaoguan cities) and the southern Pearl River Delta (e.g. Foshan, Zhongshan, Zhuhai and Jiangmen cities), respectively. Age- and gender-specific risk maps revealed that males had a higher infection risk than females, and the infection risk was higher in adults compared to children. CONCLUSIONS: Our high-resolution risk maps of C. sinensis infection in Guangdong Province identified the spatial, temporal, age and gender heterogeneities, which can provide useful information assisting tailored control strategies.

HFSCCD: A Hybrid Neural Network for Fetal Standard Cardiac Cycle Detection in Ultrasound Videos.

In the fetal cardiac ultrasound examination, standard cardiac cycle (SCC) recognition is the essential foundation for diagnosing congenital heart disease. Previous studies have mostly focused on the detection of adult CCs, which may not be applicable to the fetus. In clinical practice, localization of SCCs needs to recognize end-systole (ES) and end-diastole (ED) frames accurately, ensuring that every frame in the cycle is a standard view. Most existing methods are not based on the detection of key anatomical structures, which may not recognize irrelevant views and background frames, results containing non-standard frames, or even it does not work in clinical practice. We propose an end-to-end hybrid neural network based on an object detector to detect SCCs from fetal ultrasound videos efficiently, which consists of 3 modules, namely Anatomical Structure Detection (ASD), Cardiac Cycle Localization (CCL), and Standard Plane Recognition (SPR). Specifically, ASD uses an object detector to identify 9 key anatomical structures, 3 cardiac motion phases, and the corresponding confidence scores from fetal ultrasound videos. On this basis, we propose a joint probability method in the CCL to learn the cardiac motion cycle based on the 3 cardiac motion phases. In SPR, to reduce the impact of structure detection errors on the accuracy of the standard plane recognition, we use XGBoost algorithm to learn the relation knowledge of the detected anatomical structures. We evaluate our method on the test fetal ultrasound video datasets and clinical examination cases and achieve remarkable results. This study may pave the way for clinical practices.

An Easy and Effective Method for Evaluating the Position of Conus Medullaris: Counting the Number of Vertebral Ossification Center Below the End of Conus Medullaris.

OBJECTIVE: This study aimed to ascertain the conus medullaris position by counting the number of ossification centers in the vertebral bodies below the conus medullaris endpoint (N) and assess its utility in screening for closed spinal dysraphism and tethered cord syndrome. METHODS: A total of 900 normal fetuses and 146 fetuses with closed spinal dysraphism or tethered cord syndrome were included in this study. The N values were tallied and compared along the spinal longitudinal plane. The receiver operating characteristic curve was utilized, and the cut-off value of N was analyzed. RESULTS: The counting of N was successfully performed in 856 normal and 146 abnormal fetuses. In the normal group, an increase in N with gestational age was observed. Specifically, in the subgroup of 17-20 wk fetuses, N was ≥6 in 117 out of 131 cases. This figure increased to 211 out of 213 in 21-24 wk and 512 out of 512 in 25-41 wk, respectively. Cases with N ≥7 accounted for 715 out of 856 fetuses in the 17-41 wk range. In the abnormal group, N was less than 7 in 152 out of 163 fetuses, showing statistical differences between the two groups. With a cut-off value of 6.5, specificity and sensitivity reached 93.3% and 83.5%. CONCLUSIONS: The counting of N was found to be a straightforward and efficient method for evaluating the position of the conus medullaris.

Hotspots and trends of microglia in Alzheimer's disease: a bibliometric analysis during 2000-2022.

BACKGROUND: Alzheimer's disease is one common type of dementia. Numerous studies have suggested a correlation between Alzheimer's disease and inflammation. Microglia mainly participate in the inflammatory response in the brain. Currently, ample evidence has shown that microglia are closely related to the occurrence and development of Alzheimer's disease. OBJECTIVE: We opted for bibliometric analysis to comprehensively summarize the advancements in the study of microglia in Alzheimer's disease, aiming to provide researchers with current trends and future research directions. METHODS: All articles and reviews pertaining to microglia in Alzheimer's disease from 2000 to 2022 were downloaded through Web of Science Core Collection. The results were subjected to bibliometric analysis using VOSviewer 1.6.18 and CiteSpace 6.1 R2. RESULTS: Overall, 7449 publications were included. The number of publications was increasing yearly. The United States has published the most publications. Harvard Medical School has published the most papers of all institutions. Journal of Alzheimer's Disease and Journal of Neuroscience were the journals with the most studies and the most commonly cited, respectively. Mt Heneka is the author with the highest productivity and co-citation. After analysis, the most common keywords are neuroinflammation, amyloid-beta, inflammation, neurodegeneration. Gut microbiota, extracellular vesicle, dysfunction and meta-analysis are the hotspots of research at the present stage and are likely to continue. CONCLUSION: NLRP3 inflammasome, TREM2, gut microbiota, mitochondrial dysfunction, exosomes are research hotspots. The relationship between microglia-mediated neuroinflammation and Alzheimer's disease have been the focus of current research and the development trend of future research.

Development of a machine learning-based model to predict hepatic inflammation in chronic hepatitis B patients with concurrent hepatic steatosis: a cohort study.

Background: With increasingly prevalent coexistence of chronic hepatitis B (CHB) and hepatic steatosis (HS), simple, non-invasive diagnostic methods to accurately assess the severity of hepatic inflammation are needed. We aimed to build a machine learning (ML) based model to detect hepatic inflammation in patients with CHB and concurrent HS. Methods: We conducted a multicenter, retrospective cohort study in China. Treatment-naive CHB patients with biopsy-proven HS between April 2004 and September 2022 were included. The optimal features for model development were selected by SHapley Additive explanations, and an ML algorithm with the best accuracy to diagnose moderate to severe hepatic inflammation (Scheuer's system ≥ G3) was determined and assessed by decision curve analysis (DCA) and calibration curve. This study is registered with ClinicalTrials.gov (NCT05766449). Findings: From a pool of 1,787 treatment-naive patients with CHB and HS across eleven hospitals, 689 patients from nine of these hospitals were chosen for the development of the diagnostic model. The remaining two hospitals contributed to two independent external validation cohorts, comprising 509 patients in validation cohort 1 and 589 in validation cohort 2. Eleven features regarding inflammation, hepatic and metabolic functions were identified. The gradient boosting classifier (GBC) model showed the best performance in predicting moderate to severe hepatic inflammation, with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI 0.83-0.88) in the training cohort, and 0.89 (95% CI 0.86-0.92), 0.76 (95% CI 0.73-0.80) in the first and second external validation cohorts, respectively. A publicly accessible web tool was generated for the model. Interpretation: Using simple parameters, the GBC model predicted hepatic inflammation in CHB patients with concurrent HS. It holds promise for guiding clinical management and improving patient outcomes. Funding: This research was supported by the National Natural Science Foundation of China (No. 82170609, 81970545), Natural Science Foundation of Shandong Province (Major Project) (No. ZR2020KH006), Natural Science Foundation of Jiangsu Province (No.BK20231118), Tianjin Key Medical Discipline (Specialty), Construction Project, TJYXZDXK-059B, Tianjin Health Science and Technology Project key discipline special, TJWJ2022XK034, and Research project of Chinese traditional medicine and Chinese traditional medicine combined with Western medicine of Tianjin municipal health and Family Planning Commission (2021022).