Neural correlates of central pain sensitization in chronic low back pain: a resting-state fMRI study.

PURPOSE: The objective of this study is to explore the neural correlates of pain sensitization in patients with chronic low back pain (cLBP). While the association between cLBP and pain sensitization has been widely reported, the underlying brain mechanism responsible for this relationship requires further investigation. METHODS: Our study included 56 cLBP patients and 56 healthy controls (HC). Functional magnetic resonance imaging data were obtained, and the voxel-wise amplitude of low-frequency fluctuation (ALFF) was calculated to identify brain alterations in cLBP patients compared to HC groups. Pearson correlation coefficients were computed to explore the association between clinical data and brain alterations. Furthermore, mediation analyses were performed to investigate the path association between brain alterations and pain-related behaviors. RESULTS: Our findings revealed that patients with cLBP exhibited higher sensitivity, attention, and catastrophizing tendencies towards pain compared to HC. Furthermore, cLBP patients displayed significantly higher ALFF in various brain regions within the "pain matrix" and the default mode network when compared to HC. The altered precuneus ALFF was positively correlated with pain intensity (R = 0.51, P<0.001) and was negatively correlated with pain sensitivity (R = -0.43, P<0.001) in cLBP patients. Importantly, the effect of altered precuneus ALFF on pain intensity was mediated by pain threshold in these patients. CONCLUSION: Our study suggests that altered neural activity in the precuneus may contribute to pain hypersensitivity, which further exacerbating pain in cLBP patients.

A Novel Approach for Apple Freshness Prediction Based on Gas Sensor Array and Optimized Neural Network.

Apple is an important cash crop in China, and the prediction of its freshness can effectively reduce its storage risk and avoid economic loss. The change in the concentration of odor information such as ethylene, carbon dioxide, and ethanol emitted during apple storage is an important feature to characterize the freshness of apples. In order to accurately predict the freshness level of apples, an electronic nose system based on a gas sensor array and wireless transmission module is designed, and a neural network prediction model using an improved Sparrow Search Algorithm (SSA) based on chaotic sequence (Tent) to optimize Back Propagation (BP) is proposed. The odor information emitted by apples is studied to complete an apple freshness prediction. Furthermore, by fitting the relationship between the prediction coefficient and the input vector, the accuracy benchmark of the prediction model is set, which further improves the prediction accuracy of apple odor information. Compared with the traditional prediction method, the system has the characteristics of simple operation, low cost, reliable results, mobile portability, and it avoids the damage to apples in the process of freshness prediction to realize non-destructive testing.

Study on modified poplar wood powder/polylactic acid high toughness green 3D printing composites.

In order to alleviate environmental pollution and the shortage of petroleum resources, improve the utilization of renewable materials, the research of biodegradable green composite materials has become a research hotspot. In this paper, Poplar Wood powder(PWP) and Polylactic acid(PLA) were selected, adding poly lactic acid graft maleic anhydride (MPLA) and Silane coupling agent KH-550 (KH550) as a compatibilizer and coupling agent to improve interface compatibility, at the same time, poly Butylenedioate-co-terephthalate (PBAT) and poly Butylene Succinate (PBS) were added to improve the toughness of the composites. The experimental results show that, the impact strength of 20 %-KMPP/PBAT/PBS composite modified by MPLA and KH550 was 20.70 kJ/m-2. Secondly, the hydrophobic angle of the composite material is as high as 112°. It is found that the high content of PWP with small particle size (200 mesh) can make it more evenly dispersed in the composite material, and the cross section of the composite material was smooth. The modified composite was 4.24$/kg, which reduced the cost by 28.07 %. The research results have opened up a new way to develop 3D printed biomass composites with low cost, high compatibility, high toughness and good environmental adaptability, and broadened the application scope and value of the composites.

Effects of soil drought and nitrogen deposition on BVOC emissions and their O3 and SOA formation for Pinus thunbergii.

Soil drought and nitrogen (N) deposition can influence the biogenic volatile organic compound (BVOC) emissions and thereby their ozone (O3) and secondary organic aerosol (SOA) formation. This study addressed their single and combined effects on BVOC emissions of Pinus thunbergii by laboratory simulation experiments. The results showed that light drought (LD, 50% soil volumetric water content (VWC)) stimulated isoprene, monoterpene, sesquiterpene, and total BVOC emissions, while moderate drought (MD, 30% and 40% VWC) and severe drought (SD, 10% and 20% VWC) inhibited their emissions (except for sesquiterpene in 20% VWC). N deposition decreased other VOC emissions and increased isoprene and sesquiterpene emissions. Total BVOCs and monoterpene were stimulated in low N deposition (LN, 2 g N/(m2·yr)) and inhibited in moderate (MN, 5 g N/(m2·yr)) and high N deposition (HN, 10 g N/(m2·yr)). Under combined treatment of soil drought and N deposition, total BVOC, monoterpene, and other VOC emissions were inhibited, sesquiterpene had no significant change, and isoprene emission was inhibited in MD combined treatment but promoted in SD. The O3 formation potential (OFP) and SOA formation potential (SOAP) from the changed BVOC emissions were calculated, OFP and SOAP of BVOC emissions and their compositions varied significantly among the treatments. Our study provided theoretical basis for assessing the impact of climate change and atmospheric pollution on BVOC emissions and their contribution to the formation of secondary atmospheric pollution.

Exploring New Characteristics: Using Deep Learning and 3D Reconstruction to Compare the Original COVID-19 and Its Delta Variant Based on Chest CT.

Purpose: Computer-aided diagnostic methods were used to compare the characteristics of the Original COVID-19 and its Delta Variant. Methods: This was a retrospective study. A deep learning segmentation model was applied to segment lungs and infections in CT. Three-dimensional (3D) reconstruction was used to create 3D models of the patient's lungs and infections. A stereoscopic segmentation method was proposed, which can subdivide the 3D lung into five lobes and 18 segments. An expert-based CT scoring system was improved and artificial intelligence was used to automatically score instead of visual score. Non-linear regression and quantitative analysis were used to analyze the dynamic changes in the percentages of infection (POI). Results: The POI in the five lung lobes of all patients were calculated and converted into CT scores. The CT scores of Original COVID-19 patients and Delta Variant patients since the onset of initial symptoms were fitted over time, respectively. The peak was found to occur on day 11 in Original COVID-19 patients and on day 15 in Delta Variant patients. The time course of lung changes in CT of Delta Variant patients was redetermined as early stage (0-3 days), progressive and peak stage (4-16 days), and absorption stage (17-42 days). The first RT-PCR negative time in Original COVID-19 patients appeared earlier than in Delta Variant patients (22 [17-30] vs. 39 [31-44], p < 0.001). Delta Variant patients had more re-detectable positive RT-PCR test results than Original COVID-19 patients after the first negative RT-PCR time (30.5% vs. 17.1%). In the early stage, CT scores in the right lower lobe were significantly different (Delta Variant vs. Original COVID-19, 0.8 ± 0.6 vs. 1.3 ± 0.6, p = 0.039). In the absorption stage, CT scores of the right middle lobes were significantly different (Delta Variant vs. Original COVID-19, 0.6 ± 0.7 vs. 0.3 ± 0.4, p = 0.012). The left and the right lower lobes contributed most to lung involvement at any given time. Conclusion: Compared with the Original COVID-19, the Delta Variant has a longer lung change duration, more re-detectable positive RT-PCR test results, different locations of pneumonia, and more lesions in the early stage, and the peak of infection occurred later.

Dynamic and Static Amplitude of Low-Frequency Fluctuation Is a Potential Biomarker for Predicting Prognosis of Degenerative Cervical Myelopathy Patients: A Preliminary Resting-State fMRI Study.

Objective: The aim of this study was to explore the clinical value of the static amplitude of low-frequency fluctuation (sALFF) and dynamic amplitude of low-frequency fluctuation (dALFF) in the identification of brain functional alterations in degenerative cervical myelopathy (DCM) patients. Methods: Voxel-wise sALFF and dALFF of 47 DCM patients and 44 healthy controls were calculated using resting-state fMRI data, and an intergroup comparison was performed. The mean of sALFF or dALFF data were extracted within the resultant clusters and the correlation analysis of these data with the clinical measures was performed. Furthermore, whole-brain-wise and region-wise multivariate pattern analyses (MVPAs) were performed to classify DCM patients and healthy controls. sALFF and dALFF were used to predict the prognosis of DCM patients. Results: The findings showed that (1) DCM patients exhibited higher sALFF within the left thalamus and putamen compared with that of the healthy controls. DCM patients also exhibited lower dALFF within bilateral postcentral gyrus compared with the healthy controls; (2) No significant correlations were observed between brain alterations and clinical measures through univariate correlation analysis; (3) sALFF (91%) and dALFF (95%) exhibited high accuracy in classifying the DCM patients and healthy controls; (4) Region-wise MVPA further revealed brain regions in which functional patterns were associated with prognosis in DCM patients. These regions were mainly located at the frontal lobe and temporal lobe. Conclusion: In summary, sALFF and dALFF can be used to accurately reveal brain functional alterations in DCM patients. Furthermore, the multivariate approach is a more sensitive method in exploring neuropathology and establishing a prognostic biomarker for DCM compared with the conventional univariate method.

Review on plant terpenoid emissions worldwide and in China.

Biogenic volatile organic compounds (BVOCs), particularly terpenoids, can significantly drive the formation of ozone (O3) and secondary organic aerosols (SOA) in the atmosphere, as well as directly or indirectly affect global climate change. Understanding their emission mechanisms and the current progress in emission measurements and estimations are essential for the accurate determination of emission characteristics, as well as for evaluating their roles in atmospheric chemistry and climate change. This review summarizes the mechanisms of terpenoid synthesis and release, biotic and abiotic factors affecting their emissions, development of emission observation techniques, and emission estimations from hundreds of published papers. We provide a review of the main observations and estimations in China, which contributes a significant proportion to the total global BVOC emissions. The review suggests the need for further research on the comprehensive effects of environmental factors on terpenoid emissions, especially soil moisture and nitrogen content, which should be quantified in emission models to improve the accuracy of estimation. In China, it is necessary to conduct more accurate measurements for local plants in different regions using the dynamic enclosure technique to establish an accurate local emission rate database for dominant tree species. This will help improve the accuracy of both national and global emission inventories. This review provides a comprehensive understanding of terpenoid emissions as well as prospects for detailed research to accurately describe terpenoid emission characteristics worldwide and in China.

Estimations and uncertainty of biogenic volatile organic compound emission inventory in China for 2008-2018.

Accurate biogenic volatile organic compound (BVOC) emission estimations are essential for developing effective air pollution control measures. Chinese BVOC emissions were calculated at a spatial resolution of 36 km × 36 km for 2008-2018 using the Model of Emissions of Gases and Aerosols from Nature. A statistical method was developed to obtain more accurate emission rates based on large numbers of observations from China and other countries. The most detailed and accurate vegetation investigations at high resolutions were used to determine the distributions of leaf biomass and coverage for 82 vegetation species and types. The results show that the national BVOCs emissions in China in 2018 were large, 58.89 Tg, with isoprene, monoterpene, sesquiterpene, and other VOCs accounting for 63.60%, 11.35%, 2.18%, and 22.87% of the emissions, respectively. Broadleaf trees exhibited the largest isoprene and total BVOC emissions. The biogenic emissions and compositions displayed strong seasonal variations, and isoprene was significantly more sensitive to seasonality. The emissions were concentrated in the Greater Khingan Mountain, Changbai Mountain, Qinling Mountain, southeast and southwest forest areas, and Hainan Province because of their larger distributions of broadleaf trees with higher emission potentials. During 2008-2018, BVOC emissions increased by 20.18% at an annual rate of 2.03%, and isoprene exhibited the greatest enhancement of 32.67%, which was primarily due to the increase in leaf biomass. The regions with the largest growth were distributed in the Greater Khingan and Changbai Mountains, and the Sichuan, Hunan, and Hubei Provinces, which was primarily the result of the substantial increase in volumes of trees with higher emission rates. The uncertainty of our estimates was evaluated by comparing the applied basal emission factors, vegetation coverages, meteorological data, and emission algorithms from previous studies, and it was estimated to be approximately -36.5-4.6%.

Development of Therapeutic Chimeric Uricase by Exon Replacement/Restoration and Site-Directed Mutagenesis.

The activity of urate oxidase was lost during hominoid evolution, resulting in high susceptibility to hyperuricemia and gout in humans. In order to develop a more "human-like" uricase for therapeutic use, exon replacement/restoration and site-directed mutagenesis were performed to obtain porcine-human uricase with higher homology to deduced human uricase (dHU) and increased uricolytic activity. In an exon replacement study, substitution of exon 6 in wild porcine uricase (wPU) gene with corresponding exon in dhu totally abolished its activity. Substitutions of exon 5, 3, and 1-2 led to 85%, 60%, and 45% loss of activity, respectively. However, replacement of exon 4 and 7-8 did not significantly change the enzyme activity. When exon 5, 6, and 3 in dhu were replaced by their counterparts in wpu, the resulting chimera H1-2P3H4P5-6H7-8 was active, but only about 28% of wPU. Multiple sequence alignment and homology modeling predicted that mutations of E24D and E83G in H1-2P3H4P5-6H7-8 were favorable for further increase of its activity. After site-directed mutagenesis, H1-2P3H4P5-6H7-8 (E24D & E83G) with increased homology (91.45%) with dHU and higher activity and catalytic efficiency than the FDA-approved porcine-baboon chimera (PBC) was obtained. It showed optimum activity at pH 8.5 and 35 °C and was stable in a pH range of 6.5-11.0 and temperature range of 20-40 °C.

Multivariate analyses to assess the effects of surgeon and hospital volume on cancer survival rates: a nationwide population-based study in Taiwan.

BACKGROUND: Positive results between caseloads and outcomes have been validated in several procedures and cancer treatments. However, there is limited information available on the combined effects of surgeon and hospital caseloads. We used nationwide population-based data to explore the association between surgeon and hospital caseloads and survival rates for major cancers. METHODOLOGY: A total of 11,677 patients with incident cancer diagnosed in 2002 were identified from the Taiwan National Health Insurance Research Database. Survival analysis, the Cox proportional hazards model, and propensity scores were used to assess the relationship between 5-year survival rates and different caseload combinations. RESULTS: Based on the Cox proportional hazard model, cancer patients treated by low-volume surgeons in low-volume hospitals had poorer survival rates, and hazard ratios ranged from 1.3 in head and neck cancer to 1.8 in lung cancer after adjusting for patients' demographic variables, co-morbidities, and treatment modality. When analyzed using the propensity scores, the adjusted 5-year survival rates were poorer for patients treated by low-volume surgeons in low-volume hospitals, compared to those treated by high-volume surgeons in high-volume hospitals (P<0.005). CONCLUSIONS: After adjusting for differences in the case mix, cancer patients treated by low-volume surgeons in low-volume hospitals had poorer 5-year survival rates. Payers may implement quality care improvement in low-volume surgeons.

Effect of individual and neighborhood socioeconomic status on oral cancer survival.

This population-based study investigated the relationship between individual and neighborhood socioeconomic status (SES) and oral cancer mortality. A population-based follow-up study was conducted of 3607 oral cancer patients (predominantly male) who were diagnosed between 2004 and 2005. Each patient was traced to death or for 2 years. Individual SES was defined by enrollee category. Neighborhood SES was defined by income, and numbers of doctors, and neighborhoods were grouped into advantaged and disadvantaged areas. The Cox proportional hazards model was used to compare the death-free survival rate between the different SES groups after adjusting for possible confounding and risk factors. In oral cancer patients aged below 65 years, death rates among those with low SES were highest in disadvantaged neighborhoods. After adjusting for patient characteristics (age, gender, Charlson Comorbidity Index Score, urbanization, and area of residence), tumor extent, treatment modalities (operation, adjuvant therapy), hospital characteristics (ownership, teaching level, caseload), and year of diagnosis, oral cancer patients with low individual SES in disadvantaged neighborhoods conferred a 1.46- to 1.64-fold higher risk for death, compared with patients with high individual SES in advantaged neighborhoods. No statistically significant difference was found in risk of death between different SES groups in patients aged 65 and above. Our findings indicate that oral cancer patients with low individual SES have the highest risk of mortality even under a universal health-care system. Public health strategies and welfare policies must continue to focus on this vulnerable group.