Prevalence and determinants of prehospital delay among stroke patients in mainland China: A systematic review and meta-analysis of the study protocol.

BACKGROUND: Prehospital delay is one of the most serious problems in the treatment of stroke patients. In China, although hospitals at all levels have promoted the construction of stroke centers, pre-hospital delays are still very common. As the primary cause of death and disability, stroke not only brings great harm to patients themselves, but also brings a heavy burden on social progress and economic development, it is important to understand the prevalence and determinants of prehospital delay among stroke patients. Therefore, this review aims to determine the pooled prevalence and determinants of prehospital delay in mainland China. METHODS: A systematic review of eligible articles will be conducted using preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines. A comprehensive literature search will be conducted in PubMed, Embase, Cochrane, web of science, China National Knowledge Infrastructure (CNKI), Wanfang, Weipu (VIP) and Chinese Biomedicine Iiterature databas (CBM) databases. The quality of the articles included in the review will be evaluated using the Newcastle-Ottawa Scale (NOS). The pooled prevalence of prehospital delay, and odds ratio and their 95% confidence intervals for relevant influencing factors, will be calculated using RevMan 5.3 software. The existence of heterogeneity among studies will be assessed by computing p-values of Higgins's I2 test statistics and Cochran's Q-statistics. Sensitivity analysis and subgroup analysis will be conducted based on study quality to investigate the possible sources of heterogeneity. Publication bias will be evaluated by funnel chart and by Egger's regression test. This review protocol has been registered PROSPERO (CRD42023484580). DISCUSSION: By collecting and summarizing information on prehospital delay among stroke patients can be a step towards a better understanding of the prevalence of prehospital delay among stroke patients in mainland China and how the associated factors influence the prevalence of prehospital delay. Therefore, a rapid, accurate diagnosis Stroke, timely pre-hospital first aid, the treatment process forward, for the patient It has great significance. This summarized finding at the national level will provide new clues for intervention to reduce the rate of pre-hospital delay of stroke patients, and is expected to further improve the treatment effect of stroke patients.

Passive Inclination Sensor Based on a Patch Antenna with a Reconfigurable Water Load.

In order to ensure the safety and preserve the value of historical buildings, inclination is an essential parameter during the continuous structural health monitoring process. However, the wire and price of a traditional sensor limit application. This paper proposes a low-cost inclination sensor based on a patch antenna with a reconfigurable water load. Only the water directly on the antenna is considered effective. The different volume of the effective water load, which is determined by the inclination of the attached surface, will affect the effective permittivity of the dielectric plate of the patch antenna, further causing a variation in the resonant frequency. Therefore, the proposed antenna sensor can monitor the inclination of the attached surface by interrogating the resonant frequency. The working mechanism is first clarified by theoretically investigating the relationship between the dielectric properties and the inclination of the covering medium. The antenna sensor is then simulated using High-Frequency Structure Simulator ver.15 (HFSS 15), which helps to determine geometric parameters and confirm accuracy and sensitivity. An experiment has been conducted based on the design verified in the simulation. The inclination detection shows a correlation coefficient of 0.9771 with a sensitivity of 7.92 MHz/°, indicating a potential for real application.

Assessing the impact of temperature on acute exacerbation of chronic obstructive pulmonary disease hospitalizations in residents of Panzhihua City: a multi-districts study using a distributed lag non-linear model.

BACKGROUND: Temperature fluctuations can impact the occurrence and progression of respiratory system diseases. However, the current understanding of the impact of temperature on acute exacerbation of chronic obstructive pulmonary disease (AECOPD) remains limited. Therefore, our study aims to investigate the relationship between daily mean temperature (DMT) and the risk of AECOPD hospitalizations within Panzhihua City. METHODS: We systematically collected data on AECOPD hospitalizations at Panzhihua Central Hospital from 2015 to 2020 and meteorological factors across Panzhihua City's districts. A two-stage analysis method was used to establish a distributed lag non-linear model to elucidate the influence of DMT on the frequency of admissions for AECOPD. Subgroup analyses were conducted by gender and age to identify populations potentially susceptible to the impact of DMT. RESULTS: A total of 5299 AECOPD hospitalizations cases were included. The DMT and the risk of AECOPD hospitalization showed a non-linear exposure-response pattern, with low temperatures exacerbating the risk of hospitalizations. The lag effects of low temperature and relatively low temperature peaked at 2th day, with the lag effects disappearing at 16-17 days. Females and elders aged ≥ 65 years were more sensitive to effects of low and relatively low temperature at lag 0-4 days, while male AECOPD patients exhibited longer lasting lag effects. CONCLUSIONS: Low temperatures are associated with an increased risk of AECOPD hospitalizations. Females or elders aged ≥ 65 years with chronic obstructive pulmonary disease should pay more attention to taking protective measures in cold environments. These findings are crucial for the formulation of public health policies, as they will help significantly alleviate the burden of AECOPD and improve respiratory health in the face of climate challenges.

JIMR: Joint Semantic and Geometry Learning for Point Scene Instance Mesh Reconstruction.

Point scene instance mesh reconstruction is a challenging task since it requires both scene-level instance segmentation and instance-level mesh reconstruction from partial observations simultaneously. Previous works either adopt a detection backbone or a segmentation one, and then directly employ a mesh reconstruction network to produce complete meshes from incomplete instance point clouds. To further boost the mesh reconstruction quality with both local details and global smoothness, in this work, we propose JIMR, a joint framework with two cascaded stages for semantic and geometry understanding. In the first stage, we propose to perform both instance segmentation and object detection simultaneously. By making both tasks promote each other, this design facilitates subsequent mesh reconstruction by providing more precisely-segmented instance points and better alignment benefiting from predicted complete bounding boxes. In the second stage, we propose a complete-then-reconstruct procedure, where the completion module explicitly disentangles completion from reconstruction, and enables the usage of pre-trained weights of existing powerful completion and reconstruction networks. Moreover, we propose a comprehensive confidence score to filter proposals considering the quality of instance segmentation, bounding box detection, semantic classification, and mesh reconstruction at the same time. Experiments show that our proposed JIMR outperforms state-of-the-art methods regarding instance reconstruction qualitatively and quantitatively.

Long-term exposure to particulate matter is associated with elevated blood pressure: Evidence from the Chinese plateau area.

Background: Ambient air pollution could increase the risk of hypertension; however, evidence regarding the relationship between long-term exposure to particulate matter and elevated blood pressure in plateau areas with lower pollution levels is limited. Methods: We assessed the associations of long-term exposure to particulate matter (PM, PM1, PM2.5, and PM10) with hypertension, diastolic blood pressure (DBP), systolic blood pressure (SBP) and pulse pressure (PP) in 4.235 Tibet adults, based on the baseline of the China multi-ethnic cohort study (CMEC) in Lhasa city, Tibet from 2018-19. We used logistic regression and linear regression models to evaluate the associations of ambient PM with hypertension and blood pressure, respectively. Results: Long-term exposure to PM1, PM2.5, and PM10 is positively associated with hypertension, DBP, and SBP, while negatively associated with PP. Among these air pollutants, PM10 had the strongest effect on hypertension, DBP, and SBP, while PM2.5 had the strongest effect on PP. The results showed for hypertension odds ratio (OR) = 1.99; 95% confidence interval (CI) = 1.58, 2.51 per interquartile range (IQR) µg/m3 increase in PM1, OR = 1.93; 95% CI = 1.55, 2.40 per IQR µg/m3 increase in PM2.5, and OR = 2.12; 95% CI = 1.67, 2.68 per IQR µg/m3 increase in PM10. Conclusions: Long-term exposure to ambient air pollution was associated with an increased risk of hypertension, elevated SBP and DBP levels, and decreased PP levels. To reduce the risk of hypertension and PP reduction, attention should be paid to air quality interventions in plateau areas with low pollution levels.

High CD133 expression in proximal tubular cells in diabetic kidney disease: good or bad?

BACKGROUND: Proximal tubular cells (PTCs) play a critical role in the progression of diabetic kidney disease (DKD). As one of important progenitor markers, CD133 was reported to indicate the regeneration of dedifferentiated PTCs in acute kidney disease. However, its role in chronic DKD is unclear. Therefore, we aimed to investigate the expression patterns and elucidate its functional significance of CD133 in DKD. METHODS: Data mining was employed to illustrate the expression and molecular function of CD133 in PTCs in human DKD. Subsequently, rat models representing various stages of DKD progression were established. The expression of CD133 was confirmed in DKD rats, as well as in human PTCs (HK-2 cells) and rat PTCs (NRK-52E cells) exposed to high glucose. The immunofluorescence and flow cytometry techniques were utilized to determine the expression patterns of CD133, utilizing proliferative and injury indicators. After overexpression or knockdown of CD133 in HK-2 cells, the cell proliferation and apoptosis were detected by EdU assay, real-time cell analysis and flow analysis. Additionally, the evaluation of epithelial, progenitor cell, and apoptotic indices was performed through western blot and quantitative RT-PCR analyses. RESULTS: The expression of CD133 was notably elevated in both human and rat PTCs in DKD, and this expression increased as DKD progressed. CD133 was found to be co-expressed with CD24, KIM-1, SOX9, and PCNA, suggesting that CD133+ cells were damaged and associated with proliferation. In terms of functionality, the knockdown of CD133 resulted in a significant reduction in proliferation and an increase in apoptosis in HK-2 cells compared to the high glucose stimulus group. Conversely, the overexpression of CD133 significantly mitigated high glucose-induced cell apoptosis, but had no impact on cellular proliferation. Furthermore, the Nephroseq database provided additional evidence to support the correlation between CD133 expression and the progression of DKD. Analysis of single-cell RNA-sequencing data revealed that CD133+ PTCs potentially play a role in the advancement of DKD through multiple mechanisms, including heat damage, cell microtubule stabilization, cell growth inhibition and tumor necrosis factor-mediated signaling pathway. CONCLUSION: Our study demonstrates that the upregulation of CD133 is linked to cellular proliferation and protects PTC from apoptosis in DKD and high glucose induced PTC injury. We propose that heightened CD133 expression may facilitate cellular self-protective responses during the initial stages of high glucose exposure. However, its sustained increase is associated with the pathological progression of DKD. In conclusion, CD133 exhibits dual roles in the advancement of DKD, necessitating further investigation.

A One Health perspective on the use of genotypic methods for antimicrobial resistance prediction.

Antimicrobial resistance is a global One Health concern with critical implications for the health of humans, animals, and the environment. Phenotypic methods of bacterial culture and antimicrobial susceptibility testing remain the gold standards for the detection of antimicrobial resistance and appropriate patient care; however, genotypic-based methods, such as PCR, whole genome sequencing, and metagenomic sequencing, for detection of genes conferring antimicrobial resistance are increasingly available without inclusion of appropriate standards for quality or interpretation. Misleading test results may lead to inappropriate antimicrobial treatment and, in turn, poor patient outcomes and the potential for increased incidence of antimicrobial resistance. This article explores the current landscape of clinical and methodological aspects of antimicrobial susceptibility testing and genotypic antimicrobial resistance test methods. Additionally, it describes the limitations associated with employing genotypic-based test methods in the management of veterinary patients from a One Health perspective. The companion Currents in One Health by Maddock et al, AJVR, March 2024, addresses current and future needs for veterinary antimicrobial resistance research.

Current state and future directions for veterinary antimicrobial resistance research.

Antimicrobial resistance (AMR) is a critical One Health concern with implications for human, animal, plant, and environmental health. Antimicrobial susceptibility testing (AST), antimicrobial resistance testing (ART), and surveillance practices must be harmonized across One Health sectors to ensure consistent detection and reporting practices. Veterinary diagnostic laboratory stewardship, clinical outcomes studies, and training for current and future generations of veterinarians and laboratorians are necessary to minimize the spread of AMR and move veterinary medicine forward into an age of better antimicrobial use practices. The purpose of this article is to describe current knowledge gaps present in the literature surrounding ART, AST, and clinical or surveillance applications of these methods and to suggest areas where AMR research can fill these knowledge gaps. The related Currents in One Health by Maddock et al, JAVMA, March 2024, addresses current limitations to the use of genotypic ART methods in clinical veterinary practice.

Associations of long-term exposure to air pollution and physical activity with the risk of systemic inflammation-induced multimorbidity in Chinese adults: results from the China multi-ethnic cohort study (CMEC).

OBJECTIVE: Previous studies proved the effect of long-term exposure to air pollution or physical activity (PA) on the risk of systemic inflammation-induced multimorbidity (SIIM), while the evidence regarding their joint effects was rare, especially in low- and middle-income countries. Therefore, we aimed to examine the extent of interaction or joint relations of PA and air pollution with SIIM. METHODS: This study included 72,172 participants from China Multi-Ethnic Cohort.The average concentrations of ambient particulate matter pollutants (PM1, PM2.5, and PM10) were estimated using satellite-based random forest models. Self-reported information on a range of physical activities related to occupation, housework, commuting, and leisure activities was collected by an interviewer-administered questionnaire. A total of 11 chronic inflammatory systemic diseases were assessed based on self-reported lifetime diagnosis or medical examinations. SIIM was defined as having ≥ 2 chronic diseases related to systemic inflammation. Logistic regression models were used to assess the complex associations of air pollution particulate matter and PA with SIIM. RESULTS: We found positive associations between long-term air pollution particulates exposure and SIIM, with odds ratios (95%CI) of 1.07 (1.03 to 1.11), 1.18 (1.13 to 1.24), and 1.08 (1.05 to 1.12) per 10 µg/m3 increase in PM1, PM2.5, and PM10. No significant multiplicative interaction was found between ambient air pollutant exposure and PA on SIIM, whereas negative additive interaction was observed between long-term exposure to PM2.5 and PA on SIIM. The positive associations between low volume PA and SIIM were stronger among those exposed to high-level air pollution particulates. Compared with individuals engaged in high volume PA and exposed to low-level ambient air pollutants, those engaged in low volume PA and exposed to high-level ambient air pollutants had a higher risk of SIIM (OR = 1.49 in PM1 exposure, OR = 1.84 in PM2.5 exposure, OR = 1.19 in PM10 exposure). CONCLUSIONS: Long-term (3 years average) exposure to PM1, PM2.5, and PM10 was associated with an increased risk of SIIM. The associations were modified by PA, highlighting PA's importance in reducing SIIM for all people, especially those living in high-level air pollution regions.

Efficient Crowd Counting via Dual Knowledge Distillation.

Most researchers focus on designing accurate crowd counting models with heavy parameters and computations but ignore the resource burden during the model deployment. A real-world scenario demands an efficient counting model with low-latency and high-performance. Knowledge distillation provides an elegant way to transfer knowledge from a complicated teacher model to a compact student model while maintaining accuracy. However, the student model receives the wrong guidance with the supervision of the teacher model due to the inaccurate information understood by the teacher in some cases. In this paper, we propose a dual-knowledge distillation (DKD) framework, which aims to reduce the side effects of the teacher model and transfer hierarchical knowledge to obtain a more efficient counting model. First, the student model is initialized with global information transferred by the teacher model via adaptive perspectives. Then, the self-knowledge distillation forces the student model to learn the knowledge by itself, based on intermediate feature maps and target map. Specifically, the optimal transport distance is utilized to measure the difference of feature maps between the teacher and the student to perform the distribution alignment of the counting area. Extensive experiments are conducted on four challenging datasets, demonstrating the superiority of DKD. When there are only approximately 6% of the parameters and computations from the original models, the student model achieves a faster and more accurate counting performance as the teacher model even surpasses it.

The impact of ambient air pollution on hospital admissions, length of stay and hospital costs for patients with diabetes mellitus and comorbid respiratory diseases in Panzhihua, Southwest China.

Background: There is limited evidence on association between air pollutants and hospital admissions, hospital cost and length of stay (LOS) among patients with diabetes mellitus (DM) and comorbid respiratory diseases (RD), especially in low- and middle-income countries (LMICs) with low levels of air pollution. Methods: Daily data on RD-DM patients were collected in Panzhihua from 2016 to 2020. A generalised additive model (GAM) was used to explore the effect of air pollutants on daily hospital admissions, LOS and hospital cost. Attributable risk was employed to estimate RD-DM's burden due to exceeding air pollution exposure, using both 0 microgrammes per cubic metre (µg/m3) and WHO's 2021 air quality guidelines as reference. Results: For each 10 ug/m3 increase of particles with an aerodynamic diameter <2.5 micron (µm) (PM2.5), particles with an aerodynamic diameter <10 µm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3), the admissions of RD-DM patients increased by 7.25% (95% CI = 4.26 to 10.33), 5.59% (95% CI = 3.79 to 7.42), 10.10% (95% CI = 7.29 to 12.98), 12.33% (95% CI = 8.82 to 15.95) and -2.99% (95% CI = -4.08 to -1.90); per 1 milligramme per cubic metre (mg/m3) increase of carbon monoxide (CO) corresponded to a 25.77% (95% CI = 17.88 to 34.19) increment for admissions of RD-DM patients. For LOS and hospital cost, the six air pollutants showed similar effect. Given 0 µg/m3 as the reference, NO2 showed the maximum attributable fraction of 32.68% (95% CI = 25.12 to 39.42%), corresponding to an avoidable burden of 5661 (95% CI = 3611 to 5860) patients with RD-DM. Conclusions: There is an association between PM2.5, PM10, SO2, NO2, and CO with increased hospital admissions, LOS and hospital cost in patients with RD-DM. Disease burden of RD-DM may be improved by formulating policies related to air pollutants exposure reduction, especially in LMICs with low levels of air pollution.

Mechanisms of the PD-1/PD-L1 pathway in itch: From acute itch model establishment to the role in chronic itch in mouse.

Programmed cell death receptor/ligand 1 (PD-1/PD-L1) blockade therapy for various cancers induces itch. However, few studies have evaluated the mechanism underlying PD-1/PD-L1 inhibitor-induced itch. This study aimed to establish and evaluate a mouse model of acute itch induced by PD-1/PD-L1 inhibitors and to explore the role of the PD-1/PD-L1 pathway in chronic itch. The intradermal injection of the PD-1/PD-L1 small molecule inhibitors, or anti-PD-1/PD-L1 antibodies in the nape of the neck in the mice elicited intense spontaneous scratches. The model was evaluated using pharmacological methods. The number of scratches was reduced by naloxone but not by antihistamines or the transient receptor potential (TRP) channel inhibitor. Moreover, the PD-1 receptor was detected in the spinal cord of the mouse models of chronic itch that exhibited acetone, diethyl ether, and water (AEW)-induced dry skin, imiquimod-induced psoriasis, and 1-fluoro-2,4-dinitrobenzene (DNFB)-induced allergic contact dermatitis. Intrathecal PD-L1 (1 µg, 4 times a week for 1 week) suppressed the activation of the microglia in the spinal dorsal horn to relieve the chronic itch that was elicited by imiquimod-induced psoriasis and DNFB-induced allergic contact dermatitis. Although the activation of the microglia in the spinal dorsal horn was not detected in the AEW-treated mice, intrathecal PD-L1 still reduced the number of scratches that were elicited by AEW. Our findings suggest that histamine receptor inhibitors or TRP channel inhibitors have limited effects on PD-1/PD-L1 inhibitor-induced itch and that spinal PD-1 is important for the spinal activation of the microglia, which may underlie chronic itch.

Urban-rural differences in the association between long-term exposure to ambient particulate matter (PM) and malnutrition status among children under five years old: A cross-sectional study in China.

Background: The evidence regarding the relationship between postnatal exposure of air pollution and child malnutrition indicators, as well as the corresponding urban-rural disparities, is limited, especially in low-pollution area of low- and middle-income countries (LMICs). Therefore, our aim was to contrast the effect estimates of varying ambient particulate matter (PM) on malnutrition indicators between urban and rural areas in Tibet, China. Methods: Six malnutrition indicators were evaluated in this study, namely, Z-scores of height for age (HFA), Z-scores of weight for age (WFA), Z-scores of weight for height (WFH), stunting, underweight, and wasting. Exposure to particles with an aerodynamic diameter ≤2.5 micron (µm) (PM2.5), particles with an aerodynamic diameter ≤10 µm (PM10) and particles with an aerodynamic diameter between 2.5 and 10 µm (PMc) was estimated using satellite-based random forest models. Linear regression and logistic regression models were used to assess the associations between PM and the above malnutrition indicators. Furthermore, the effect estimates of different PM were contrasted between urban and rural areas. Results: A total of 2511 children under five years old were included in this study. We found long-term exposure to PM2.5, PMc, and PM10 was associated with an increased risk of stunting and a decreased risk of underweight. Of these air pollutants, PMc had the strongest association for Z-scores of HFA and stunting, while PM2.5 had the strongest association for underweight. The results showed that the odds ratio (OR) for stunting were 1.36 (95% confidence interval (CI) = 1.06 to 1.75) per interquartile range (IQR) microgrammes per cubic metre (µg/m3) increase in PM2.5, 1.80 (95% CI = 1.30 to 2.50) per IQR µg/m3 increase in PMc and 1.55 (95% CI = 1.17 to 2.05) per IQR µg/m3 increase in PM10. The concentrations of PM were higher in urban areas, and the effects of PM on malnutrition indicators among urban children were higher than those of rural children. Conclusions: Our results suggested that PM exposure might be an important trigger of child malnutrition. Further prospective researches are needed to provide important scientific literature for understanding child malnutrition risk concerning postnatal exposure of air pollutants and formulating synthetically social and environmental policies for malnutrition prevention.

Extended Spectrum ß-Lactamase-Producing Enterobacterales of Shrimp and Salmon Available for Purchase by Consumers in Canada-A Risk Profile Using the Codex Framework.

The extended-spectrum ß-lactamase (ESBL)-producing Enterobacterales (ESBL-EB) encompass several important human pathogens and are found on the World Health Organization (WHO) priority pathogens list of antibiotic-resistant bacteria. They are a group of organisms which demonstrate resistance to third-generation cephalosporins (3GC) and their presence has been documented worldwide, including in aquaculture and the aquatic environment. This risk profile was developed following the Codex Guidelines for Risk Analysis of Foodborne Antimicrobial Resistance with the objectives of describing the current state of knowledge of ESBL-EB in relation to retail shrimp and salmon available to consumers in Canada, the primary aquacultured species consumed in Canada. The risk profile found that Enterobacterales and ESBL-EB have been found in multiple aquatic environments, as well as multiple host species and production levels. Although the information available did not permit the conclusion as to whether there is a human health risk related to ESBLs in Enterobacterales in salmon and shrimp available for consumption by Canadians, ESBL-EB in imported seafood available at the retail level in Canada have been found. Surveillance activities to detect ESBL-EB in seafood are needed; salmon and shrimp could be used in initial surveillance activities, representing domestic and imported products.

Effect of down-regulation of let-7c/g on triggering a double-negative feedback loop and promoting restenosis.

BACKGROUND: Excessive proliferation and migration of vascular smooth muscle cells (VSMCs) are the main causes of restenosis (RS) in diabetic lower extremity arterial disease (LEAD). However, the relevant pathogenic mechanisms are poorly understood. METHODS: In this study, we introduced a "two-step injury protocol" rat RS model, which started with the induction of atherosclerosis (AS) and was followed by percutaneous transluminal angioplasty (PTA). Hematoxylin-eosin (HE) staining and immunohistochemistry staining were used to verify the form of RS. Two-step transfection was performed, with the first transfection of Lin28a followed by a second transfection of let-7c and let-7g, to explore the possible mechanism by which Lin28a exerted effects. 5-ethynyl-2΄-deoxyuridine (EdU) and Transwell assay were performed to evaluate the ability of proliferation and migration of VSMCs. Western blotting and quantitative real-time polymerase chain reaction (qRT-PCR) were performed to detect the expression of Lin28a protein and let-7 family members. RESULTS: Using a combination of in vitro and in vivo experiments, we discovered that let-7c, let-7g, and microRNA98 (miR98) were downstream targets of Lin28a. More importantly, decreased expression of let-7c/let-7g increased Lin28a, leading to further inhibition of let-7c/let-7g. We also found an increased level of let-7d in the RS pathological condition, suggesting that it may function as a protective regulator of the Lin28a/let-7 loop by inhibiting the proliferation and migration of VSMCs. CONCLUSION: These findings indicated the presence of a double-negative feedback loop consisting of Lin28a and let-7c/let-7g, which may be responsible for the vicious behavior of VSMCs in RS.

Development and validation of prognostic risk prediction models for hepatocellular carcinoma patients treated with immune checkpoint inhibitors based on a systematic review and meta-analysis of 47 cohorts.

Objective: To identify the risk factors associated with prognosis in patients with hepatocellular carcinoma (HCC) treated with immune checkpoint inhibitors (ICI) via meta-analysis. And to construct prediction models to aid in the prediction and improvement of prognosis. Methods: We searched PubMed, Embase, Web of Science and Cochrane Library for relevant studies from inception to March 29, 2023. After completing literature screening and data extraction, we performed meta-analysis, sensitivity analysis, and subgroup analysis to identify risk factors associated with OS and PFS. Using the pooled hazard ratio value for each risk factor, we constructed prediction models, which were then validated using datasets from 19 centers in Japan and two centers in China, comprising a total of 204 patients. Results: A total of 47 studies, involving a total of 7649 ICI-treated HCC patients, were included in the meta-analysis. After analyzing 18 risk factors, we identified AFP, ALBI, NLR, ECOG performance status, Child-Pugh stage, BCLC stage, tumor number, vascular invasion and combination therapy as predictors for OS prediction model, while AFP, ALBI, NLR, ECOG performance status, Child-Pugh stage, BCLC stage, tumor number and vascular invasion were selected as predictors for PFS model. To validate the models, we scored two independent cohorts of patients using both prediction models. Our models demonstrated good performance in these cohorts. In addition, in the pooled cohort of 204 patients, Our models also showed good performance with area under the curve (AUC) values of 0.712, 0.753, and 0.822 for the OS prediction model at 1-year, 2-year, and 3-year follow-up points, respectively, and AUC values of 0.575, 0.749 and 0.691 for the PFS prediction model Additionally, the calibration curve, decision curve analysis, and Kaplan-Meier curves in the pooled cohort all supported the validity of both models. Conclusion: Based on the meta-analysis, we successfully constructed the OS and PFS prediction models for ICI-treated HCC patients. We also validated the models externally and observed good discrimination and calibration. The model's selected indicators are easily obtainable, making them suitable for further application in clinical practice.

The drivers of carbon emissions in China: the perspective of fiscal decentralization.

Fiscal decentralization determines government behavior considerably by being a major factor influencing China's carbon emissions. We analyze the impact of fiscal decentralization on carbon emissions on basis of the dataset of 30 Chinese areas for the period 2008 to 2020. We reveal that fiscal decentralization significantly raises carbon emissions, but worsens carbon emission efficiency. The impact of fiscal decentralization on carbon emissions varies significantly by geographical location and economic development level shocks. Local governments increase the expenditure on economic services while reducing the expenditure on basic public services as a result of the fiscal decentralization scheme, which is an important reason why the carbon emission level cannot be effectively improved. As such, it is necessary to keep pace with the times, and build a service-oriented government, and capitalize on the capabilities of local governments in terms of their service duties so as to maximize carbon reduction.

Point Set Self-Embedding.

This work presents an innovative method for point set self-embedding, that encodes the structural information of a dense point set into its sparser version in a visual but imperceptible form. The self-embedded point set can function as the ordinary downsampled one and be visualized efficiently on mobile devices. Particularly, we can leverage the self-embedded information to fully restore the original point set for detailed analysis on remote servers. This task is challenging, since both the self-embedded point set and the restored point set should resemble the original one. To achieve a learnable self-embedding scheme, we design a novel framework with two jointly-trained networks: one to encode the input point set into its self-embedded sparse point set and the other to leverage the embedded information for inverting the original point set back. Further, we develop a pair of up-shuffle and down-shuffle units in the two networks, and formulate loss terms to encourage the shape similarity and point distribution in the results. Extensive qualitative and quantitative results demonstrate the effectiveness of our method on both synthetic and real-scanned datasets. The source code and trained models will be publicly available at https://github.com/liruihui/Self-Embedding.