Measuring the quality of transitional care based on elderly patients' experiences with the partners at care transitions measure: a cross-sectional survey.

BACKGROUND: The quality of transitional care is closely related to the health outcomes of patients, and understanding the status of transitional care for patients is crucial to improving the health outcomes of patients. Therefore, this study aims to investigate the quality of transitional care in elderly patients with chronic diseases and analyze its influencing factors, to provide a basis for improving transitional care services. METHODS: This is a cross-sectional study. We used the Chinese version of the Partners at Care Transitions Measure (PACT-M) to survey patients with chronic diseases aged 60 years and older who were about to be discharged from five tertiary hospitals in Henan and Shanxi provinces. We used the mean ± standard deviation to describe the quality of transitional care, t-test or one-way ANOVA, and regression analysis to explore the factors affecting the quality of transitional care for patients. RESULTS: 182 elderly patients with chronic diseases aged ≥ 60 years completed the PACT-M survey. The scores of PACT-M1 and PACT-M2 were (30.69 ± 7.87) and (25.59 ± 7.14) points, respectively. The results of the t-test or one-way ANOVA showed that the patient's marital status, ethnicity, religion, educational level, preretirement occupation, residence, household income per month, and living situation had an impact on the quality of transitional care for elderly patients with chronic diseases (P < 0.05). The results of regression analyses showed that patients' preretirement occupation, social support, and health status were the main influences on the quality of transitional care for elderly patients with chronic diseases (P < 0.05), and they explained 63.1% of the total variance. CONCLUSIONS: The quality of transitional care for older patients with chronic illnesses during the transition from hospital to home needs further improvement. Factors affecting the quality of transitional care included patients' pre-retirement occupation, social support, and health status. We can improve the hospital-community-family tertiary linkage service to provide coordinated and continuous transitional care for patients based on their occupation, health status, and social support to enhance the quality of transitional care and the patient's health.

The Retinal Basis of Light Aversion in Neonatal Mice.

Aversive responses to bright light (photoaversion) require signaling from the eye to the brain. Melanopsin-expressing intrinsically photosensitive retinal ganglion cells (ipRGCs) encode absolute light intensity and are thought to provide the light signals for photoaversion. Consistent with this, neonatal mice exhibit photoaversion before the developmental onset of image vision, and melanopsin deletion abolishes photoaversion in neonates. It is not well understood how the population of ipRGCs, which constitutes multiple physiologically distinct types (denoted M1-M6 in mouse), encodes light stimuli to produce an aversive response. Here, we provide several lines of evidence that M1 ipRGCs that lack the Brn3b transcription factor drive photoaversion in neonatal mice. First, neonatal mice lacking TRPC6 and TRPC7 ion channels failed to turn away from bright light, while two photon Ca2+ imaging of their acutely isolated retinas revealed reduced photosensitivity in M1 ipRGCs, but not other ipRGC types. Second, mice in which all ipRGC types except for Brn3b-negative M1 ipRGCs are ablated exhibited normal photoaversion. Third, pharmacological blockade or genetic knockout of gap junction channels expressed by ipRGCs, which reduces the light sensitivity of M2-M6 ipRGCs in the neonatal retina, had small effects on photoaversion only at the brightest light intensities. Finally, M1s were not strongly depolarized by spontaneous retinal waves, a robust source of activity in the developing retina that depolarizes all other ipRGC types. M1s therefore constitute a separate information channel between the neonatal retina and brain that could ensure behavioral responses to light but not spontaneous retinal waves.SIGNIFICANCE STATEMENT At an early stage of development, before the maturation of photoreceptor input to the retina, neonatal mice exhibit photoaversion. On exposure to bright light, they turn away and emit ultrasonic vocalizations, a cue to their parents to return them to the nest. Neonatal photoaversion is mediated by intrinsically photosensitive retinal ganglion cells (ipRGCs), a small percentage of the retinal ganglion cell population that express the photopigment melanopsin and depolarize directly in response to light. This study shows that photoaversion is mediated by a subset of ipRGCs, called M1-ipRGCs. Moreover, M1-ipRGCs have reduced responses to retinal waves, providing a mechanism by which the mouse distinguishes light stimulation from developmental patterns of spontaneous activity.

Theoretical Screening of Highly Efficient Single-Atom Catalysts Based on Covalent Triazine Frameworks for Oxygen Reduction.

Covalent triazine frameworks (CTFs) obtained from the trimerization of aromatic nitriles are expected to be the preferred carrier for single-atom catalysts (SACs). Using density functional theory methods, the oxygen reduction reaction (ORR) performance of a series of 3d, 4d, and 5d transition metals supported on the 6N or 9N pore of the CTF system [M-CTF(6N) or M-CTF(9N)] is explored. At first, 32 kinds of M-CTF(6N) and M-CTF(9N) are screened out with high thermodynamic and electrochemical stability. The binding energy of ORR intermediates and the change of Gibbs free energy in each step of the ORR are calculated. The overpotential of Pd-CTF(6N) is the lowest, which is 0.38 V. Considering that the ORR activity of M-CTFs is mainly limited by the strong binding of *OH, M-CTF(6N) and M-CTF(9N) are further modified by the OH ligand, namely, M-OH-CTF(6N) and M-OH-CTF(9N). After being modified by the OH ligand, due to the weakened binding strength of *OH, all these screened M-CTFs exhibit better ORR activity. Among them, the η values of Cu-OH-CTF(6N), Pd-OH-CTF(6N), Rh-OH-CTF(6N), Ir-OH-CTF(6N), Rh-OH-CTF(9N), and Ir-OH-CTF(9N) are 0.39, 0.38, 0.24, 0.30, 0.31, and 0.33 V, respectively, which possess better ORR activity than the Pt(111) surface (η = 0.45 V). This work highlights the great potential of CTFs as an efficient carrier for SACs.

Assessing the agreement between the partners at care transitions measure and the care transitions measure for elderly patients with chronic diseases.

BACKGROUND: Elderly patients with chronic diseases are very vulnerable during the transition from hospital to home and have a high need for transitional care. The quality of transitional care is closely related to patient health outcomes. Using appropriate scales to evaluate the quality of transitional care is important for efforts aimed at improving it. The study aimed to analyze the consistency between the Chinese version of the Partners at Care Transitions Measure (PACT-M) and the Care Transition Measure (CTM) in assessing the quality of transition care in elderly patients with chronic diseases. METHODS: This is a cross-sectional study, we used a convenience sampling method to investigate patients with chronic diseases aged ≧ 65 years who were about to be discharged from the three affiliated hospitals of Zhengzhou University in Henan Province, from August 2021 to May 2022. The sample consisted of 196 elderly patients with chronic diseases. Data were collected using a demographic survey, PACT-M, and CTM. We used EpiData 3.1 software for systematic logical error checking, SPSS 21.0 to analyze the data, and the Bland-Altman analysis to analyze the consistency of the two scales. RESULTS: The mean total scores for PACT-M and CTM were 65.52 ± 6.23 and 52.07 ± 7.26, respectively. The 95% confidence interval (CI) for the mean difference and ratios were (-31.52, 4.61) and (0.85, 1.72), with 3.57% and 5.10% of the points outside the 95% CI limits, separately. CONCLUSIONS: The difference analysis of Bland-Altman showed a good consistency of the two scales, while the rate analysis did not meet the a priori definition of good consistency, but it is very close to 5%. Therefore, the consistency of the two scales in assessing the quality of transitional care for elderly patients with chronic diseases needs to be further validated.

Representation learning of resting state fMRI with variational autoencoder.

Resting state functional magnetic resonance imaging (rsfMRI) data exhibits complex but structured patterns. However, the underlying origins are unclear and entangled in rsfMRI data. Here we establish a variational auto-encoder, as a generative model trainable with unsupervised learning, to disentangle the unknown sources of rsfMRI activity. After being trained with large data from the Human Connectome Project, the model has learned to represent and generate patterns of cortical activity and connectivity using latent variables. The latent representation and its trajectory represent the spatiotemporal characteristics of rsfMRI activity. The latent variables reflect the principal gradients of the latent trajectory and drive activity changes in cortical networks. Representational geometry captured as covariance or correlation between latent variables, rather than cortical connectivity, can be used as a more reliable feature to accurately identify subjects from a large group, even if only a short period of data is available in each subject. Our results demonstrate that VAE is a valuable addition to existing tools, particularly suited for unsupervised representation learning of resting state fMRI activity.

Identification of the Active Sites of NiCo2O4 and the Support Effect with Carbon Nanotubes for Oxygen Reduction Catalysis.

Since the active site of spinel NiCo2O4 as an oxygen reduction reaction (ORR) catalyst is under debate, the optimal active sites of NiCo2O4 are identified by investigating the catalytic properties of two models of NiCo2O4. The results indicate that the ORR activity of five studied active sites of isolated NiCo2O4 is primarily limited by excessively binding of *OH, and the CoIII and NiIII active sites of Co2+[Co3+Ni3+]O4 are identified as the optimal catalytic sites with overpotentials of 0.69 and 0.76 V, respectively. Moreover, for the investigation of the support effect, the isolated NiCo2O4 is supported on pristine and N-doped carbon nanotube (CNT and NCNT). Encouragingly, the ORR activity of CoIII and NiIII active sites is improved after NiCo2O4 is supported on CNT and NCNT due to the weakened binding of *OH. When NiCo2O4 is supported on NCNT via the CoII ion, the rate-determining step of ORR catalyzed by CoIII and NiIII sites is altered. Moreover, this CoIII site exhibits the highest ORR activity and displays an overpotential of only 0.45 V. Therefore, the support effect can improve the catalytic activity as well as change the mechanism of ORR. This study presents more insights into the active sites and ORR activity of NiCo2O4.

Doxycycline and minocycline in Helicobacter pylori treatment: A systematic review and meta-analysis.

BACKGROUND AND AIMS: The decreasing Helicobacter pylori eradication rate and the increasing antibiotic resistance trend are of great concern. Therefore, new and effective therapies are needed for H. pylori infection. We conducted a systematic review and meta-analysis to assess the efficacy and safety of semisynthetic tetracycline regimens in H. pylori treatment. METHODS: PubMed, EMBASE, and the Cochrane library were searched. The outcome indicators were the eradication rate, risk ratio (RR, ie, the risk of the semisynthetic tetracycline regimen relative to the control), and 95% confidence interval (95% CI). Controls were patients undergoing any other treatment without semisynthetic tetracycline. RESULTS: Twenty-three studies with 5240 participants were included. The eradication rates of triple regimens with semisynthetic tetracyclines in most studies were less than 70% in both the intention-to-treat (ITT) and the per-protocol (PP) analyses. The pooled eradication rates of quadruple therapies with doxycycline and controls were 95% and 84% in the PP analyses, respectively. The pooled RR associated with efficacy in the quadruple therapy with doxycycline group compared with the control group was 1.12 (95% CI: 1.04-1.20) in the PP analysis. The pooled RR of side effects in the quadruple therapy with doxycycline group compared with the control group was 1.01 (95% CI: 0.65-1.55). CONCLUSION: Seven-day and ten-day quadruple therapy with doxycycline might be an optional first-line therapy. The safety of regimens containing semisynthetic tetracyclines was relatively satisfactory. However, the triple regimen is not recommended.

Gastrointestinal endoscopy infection control strategy during COVID-19 pandemic: Experience from a tertiary medical center in China.

OBJECTIVES: Coronavirus disease 2019 (COVID-19) has spread globally and become a pandemic. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) not only infects the gastrointestinal (GI) tract and causes GI symptoms, but also increases nosocomial transmission risk during endoscopic procedures for aerosol generation. We hereby share our infection control strategies aiming to minimize COVID-19 transmission in the endoscopy center. METHODS: We established our infection control strategies based on the guidance of Chinese Society of Digestive Endoscopy and inputs from hospital infection control experts: admission control through the procedure and patient triage, environmental control to reduce possible virus exposure, proper usage of personal protective equipment (PPE), and scope disinfection and room decontamination. All endoscopic procedures accomplished during COVID-19 outbreak and progress of stepwise resumption of elective endoscopy procedures were retrospectively reviewed. RESULTS: Only urgent or semi-urgent procedures were performed during COVID-19 outbreak. After no local new-onset COVID-19 case in Beijing for four weeks, we reopened the endoscopy center for elective procedures and monitored the outbreak continuously while maintaining a sustainable endoscopy service. CONCLUSIONS: It is imperative that all endoscopy centers should establish standard infection control strategies in order to fight COVID-19 pandemic based on national guidance and academic society guidelines and tailor them to individual resources. These measures and setup can also be reserved for future pandemics.

Genome-wide analysis of Jatropha curcas MADS-box gene family and functional characterization of the JcMADS40 gene in transgenic rice.

BACKGROUND: Physic nut (Jatropha curcas), an inedible oilseed plant, is among the most promising alternative energy sources because of its high oil content, rapid growth and extensive adaptability. Proteins encoded by MADS-box family genes are important transcription factors participated in regulating plant growth, seed development and responses to abiotic stress. However, there has been no in-depth research on the MADS-box genes and their roles in physic nut. RESULTS: In our study, 63 MADS-box genes (JcMADSs) were identified in the physic nut genome, and classed into five groups (MIKCC, Mα, Mß, Mγ, MIKC*) according to phylogenetic comparison with Arabidopsis homologs. Expression profile analysis based on RNA-seq suggested that many JcMADS genes had the strongest expression in seeds, and seven of them responded in leaves to at least one abiotic stressor (drought and/or salinity) at one or more time points. Transient expression analysis and a transactivation assay indicated that JcMADS40 is a nucleus-localized transcriptional activator. Plants overexpressing JcMADS40 did not show altered plant growth, but the overexpressing plants did exhibit reductions in grain size, grain length, grain width, 1000-seed weight and yield per plant. Further data on the reduced grain size in JcMADS40-overexpressing plants supported the putative role of JcMADS genes in seed development. CONCLUSIONS: This study will be useful in order to further understand the process of MADS-box genes involved in regulating growth and development in addition to their functions in abiotic stress resistance, and will eventually provide a theoretical basis for the functional investigation and the exploitation of candidate genes for the molecular improvement of physic nut.

Interface Modulation of MoS2 /Metal Oxide Heterostructures for Efficient Hydrogen Evolution Electrocatalysis.

Developing efficient earth-abundant MoS2 based hydrogen evolution reaction (HER) electrocatalysts is important but challenging due to the sluggish kinetics in alkaline media. Herein, a strategy to fabricate a high-performance MoS2 based HER electrocatalyst by modulating interface electronic structure via metal oxides is developed. All the heterostructure catalysts present significant improvement of HER electrocatalytic activities, demonstrating a positive role of metal oxides decoration in promoting the rate-limited water dissociation step for the HER mechanism in alkaline media. The as-obtained MoS2 /Ni2 O3 H catalyst exhibits a low overpotential of 84 mV at 10 mA cm-2 and small charge-transfer resistance of 1.5 Ω in 1 m KOH solution. The current density (217 mA cm-2 ) at the overpotential of 200 mV is about 2 and 24 times higher than that of commercial Pt/C and bare MoS2 , respectively. Additionally, these MoS2 /metal oxides heterostructure catalysts show outstanding long-term stability under a harsh chronopotentiometry test. Theoretical calculations reveal the varied sensitivity of 3d-band in different transition oxides, in which Ni-3d of Ni2 O3 H is evidently activated to achieve fast electron transfer for HER as the electron-depletion center. Both electronic properties and energetic reaction trends confirm the high electroactivity of MoS2 /Ni2 O3 H in the adsorption and dissociation of H2 O for highly efficient HER in alkaline media.

Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex.

Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown to be able to predict and decode cortical responses to natural images or videos. Here, we explored an alternative deep neural network, variational auto-encoder (VAE), as a computational model of the visual cortex. We trained a VAE with a five-layer encoder and a five-layer decoder to learn visual representations from a diverse set of unlabeled images. Using the trained VAE, we predicted and decoded cortical activity observed with functional magnetic resonance imaging (fMRI) from three human subjects passively watching natural videos. Compared to CNN, VAE could predict the video-evoked cortical responses with comparable accuracy in early visual areas, but relatively lower accuracy in higher-order visual areas. The distinction between CNN and VAE in terms of encoding performance was primarily attributed to their different learning objectives, rather than their different model architecture or number of parameters. Despite lower encoding accuracies, VAE offered a more convenient strategy for decoding the fMRI activity to reconstruct the video input, by first converting the fMRI activity to the VAE's latent variables, and then converting the latent variables to the reconstructed video frames through the VAE's decoder. This strategy was more advantageous than alternative decoding methods, e.g. partial least squares regression, for being able to reconstruct both the spatial structure and color of the visual input. Such findings highlight VAE as an unsupervised model for learning visual representation, as well as its potential and limitations for explaining cortical responses and reconstructing naturalistic and diverse visual experiences.

Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision.

Convolutional neural network (CNN) driven by image recognition has been shown to be able to explain cortical responses to static pictures at ventral-stream areas. Here, we further showed that such CNN could reliably predict and decode functional magnetic resonance imaging data from humans watching natural movies, despite its lack of any mechanism to account for temporal dynamics or feedback processing. Using separate data, encoding and decoding models were developed and evaluated for describing the bi-directional relationships between the CNN and the brain. Through the encoding models, the CNN-predicted areas covered not only the ventral stream, but also the dorsal stream, albeit to a lesser degree; single-voxel response was visualized as the specific pixel pattern that drove the response, revealing the distinct representation of individual cortical location; cortical activation was synthesized from natural images with high-throughput to map category representation, contrast, and selectivity. Through the decoding models, fMRI signals were directly decoded to estimate the feature representations in both visual and semantic spaces, for direct visual reconstruction and semantic categorization, respectively. These results corroborate, generalize, and extend previous findings, and highlight the value of using deep learning, as an all-in-one model of the visual cortex, to understand and decode natural vision.

Developing a portable natural language processing based phenotyping system.

BACKGROUND: This paper presents a portable phenotyping system that is capable of integrating both rule-based and statistical machine learning based approaches. METHODS: Our system utilizes UMLS to extract clinically relevant features from the unstructured text and then facilitates portability across different institutions and data systems by incorporating OHDSI's OMOP Common Data Model (CDM) to standardize necessary data elements. Our system can also store the key components of rule-based systems (e.g., regular expression matches) in the format of OMOP CDM, thus enabling the reuse, adaptation and extension of many existing rule-based clinical NLP systems. We experimented with our system on the corpus from i2b2's Obesity Challenge as a pilot study. RESULTS: Our system facilitates portable phenotyping of obesity and its 15 comorbidities based on the unstructured patient discharge summaries, while achieving a performance that often ranked among the top 10 of the challenge participants. CONCLUSION: Our system of standardization enables a consistent application of numerous rule-based and machine learning based classification techniques downstream across disparate datasets which may originate across different institutions and data systems.

Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision.

The human visual cortex extracts both spatial and temporal visual features to support perception and guide behavior. Deep convolutional neural networks (CNNs) provide a computational framework to model cortical representation and organization for spatial visual processing, but unable to explain how the brain processes temporal information. To overcome this limitation, we extended a CNN by adding recurrent connections to different layers of the CNN to allow spatial representations to be remembered and accumulated over time. The extended model, or the recurrent neural network (RNN), embodied a hierarchical and distributed model of process memory as an integral part of visual processing. Unlike the CNN, the RNN learned spatiotemporal features from videos to enable action recognition. The RNN better predicted cortical responses to natural movie stimuli than the CNN, at all visual areas, especially those along the dorsal stream. As a fully observable model of visual processing, the RNN also revealed a cortical hierarchy of temporal receptive window, dynamics of process memory, and spatiotemporal representations. These results support the hypothesis of process memory, and demonstrate the potential of using the RNN for in-depth computational understanding of dynamic natural vision.

Task-evoked functional connectivity does not explain functional connectivity differences between rest and task conditions.

During complex tasks, patterns of functional connectivity differ from those in the resting state. However, what accounts for such differences remains unclear. Brain activity during a task reflects an unknown mixture of spontaneous and task-evoked activities. The difference in functional connectivity between a task state and the resting state may reflect not only task-evoked functional connectivity, but also changes in spontaneously emerging networks. Here, we characterized the differences in apparent functional connectivity between the resting state and when human subjects were watching a naturalistic movie. Such differences were marginally explained by the task-evoked functional connectivity involved in processing the movie content. Instead, they were mostly attributable to changes in spontaneous networks driven by ongoing activity during the task. The execution of the task reduced the correlations in ongoing activity among different cortical networks, especially between the visual and non-visual sensory or motor cortices. Our results suggest that task-evoked activity is not independent from spontaneous activity, and that engaging in a task may suppress spontaneous activity and its inter-regional correlation.

Approaches for Enhancing Wastewater Treatment of Photocatalytic Fuel Cells: A Review.

Environmental pollution and energy crises have garnered global attention. The substantial discharge of organic waste into water bodies has led to profound environmental contamination. Photocatalytic fuel cells (PFCs) enabling the simultaneous removal of refractory contaminants and recovery of the chemical energy contained in organic pollutants provides a potential strategy to solve environmental issues and the energy crisis. This review will discuss the fundamentals, working principle, and configuration development of PFCs and photocatalytic microbial fuel cells (PMFCs). We particularly focus on the strategies for improving the wastewater treatment performance of PFCs/PMFCs in terms of coupled advanced oxidation processes, the rational design of high-efficiency electrodes, and the strengthening of the mass transfer process. The significant potential of PFCs/PMFCs in various fields is further discussed in detail. This review is intended to provide some guidance for the better implementation and widespread adoption of PFC wastewater treatment technologies.

Construction and regulation of high active sites in montmorillonite composite catalyst for the removal of ofloxacin via persulfate activation.

In this study, ionic liquids (ILs) were used as organic modifiers by introducing montmorillonite nanolayers containing potential C and N active sites between the montmorillonite nanolayers. Organically modified montmorillonite (ILs-Mt-p) was further prepared by high-temperature pyrolysis under N2 and used for the removal of ofloxacin (OFL) by activated peroxymonosulfate (PMS). Combined with XPS and other characterization analyses, it was found that the catalyst materials prepared from different organic modifiers had similar surface functional groups and graphitized structures, but contained differences in the types and numbers of C and N active sites. The catalyst (3CPC-Mt-p) obtained after pyrolysis of montmorillonite modified with cetylpyridinium chloride (CPC) had optimal catalytic performance, in which graphitic C, graphitic N, and carbonyl group (C[bond, double bond]O) could synergistically promote the activation of PMS by electron transfer, and 77.3 % of OFL could be removed within 60 min. The effects of OFL concentration, initial pH, and anions on the effects of OFL removal by the 3CPC-Mt-p/PMS system were further investigated. Satisfactory degradation results were obtained over a wide pH range. Cl- promoted the system to degrade OFL, while the presence of SO42-, H2PO4- and HA showed some inhibition, but overall the 3CPC-Mt-p catalysts had a strong anti-interference ability, showing good application prospects. The quenching experiments and EPR tests showed that O2-- and 1O2 in the 3CPC-Mt-p/PMS system were the main reactive oxygen species for the degradation of OFL, and •OH was also involved in the reaction. This study provides ideas for the construction and modulation of active sites in mineral materials such as montmorillonite and broadens the application of montmorillonite composite catalysts in advanced oxidation processes for the treatment of antibiotic wastewater.

Primary Cutaneous Aspergillosis Due to Aspergillus fumigatus in an Immunocompetent Patient with Diabetes Mellitus After Tattooing: A Case Report and Review of Literature.

Background: Aspergillosis is an uncommon fungal infection in which primary cutaneous sites are very rare, and most cases occur in patients with immunocompromised status. Although primary cutaneous aspergillosis is usually encountered in immunocompromised patients, it also occurs in immunocompetent individuals. Case Presentation: We report a case of primary cutaneous aspergillosis in a 46-year-old immunocompetent woman with diabetes mellitus after tattooing. She presented with erythematous papules, papulopustules and a plaque on the right lower limb of more than two years duration which had failed to respond to antihistamine treatment. Histological examination of a skin biopsy sample showed oval spores in the corneous layer, a slightly thickened epidermis, and infiltrating lymphocytes and neutrophils around the blood vessels in the superficial dermis. Aspergillus fumigatus was isolated and identified in cultures. Clinical and biological examinations did not reveal any systemic localization of aspergillosis, ruling out a hypothesis of blood dissemination. Lesions resolved completely after systemic antifungal treatment with itraconazole. Conclusion: Clinical lesions of primary cutaneous aspergillosis are nonspecific and usually present as a variety of lesions, including macules, papules, nodules, plaques, purpura, blood blisters, and pustules. The nonspecific features and variety of lesions can lead to misdiagnosis and delayed treatment. Direct microscopy, microbiological culture, and histopathological examination are helpful for diagnosing primary cutaneous aspergillosis. Moreover, the physicians should be aware of the possibility of Aspergillus infection in tattooed cases.

Horizontal CO2 Compensation in the Yangtze River Delta Based on CO2 Footprints and CO2 Emissions Efficiency.

PURPOSE: In this study, we attempted to reduce the negative economic externalities related to Carbon Dioxide (CO2) emissions in the Yangtze River Delta region (YRD) and designed a cross-municipality responsibility-sharing mechanism. METHODS: We estimated the municipal CO2 footprints in the YRD from 2000 to 2019 based on nighttime light data and measured CO2 emissions efficiency using a super slack-based measurement (super-SBM) model. Based on this, we designed a scenario of horizontal CO2 compensation among the YRD's municipalities from the perspectives of both CO2 footprints and CO2 trading (CO2 unit prices in trading were determined based on CO2 emissions efficiency). RESULTS: The results showed the following: (1) The CO2 footprints evolution of the YRD municipalities could be divided into four categories, among which, eleven municipalities showed a decreasing trend. Thirteen municipalities stabilized their CO2 footprints. Thirteen municipalities exhibited strong growth in their CO2 footprints, whereas four municipalities maintained a low level of slow growth. (2) Spatially, CO2 emissions efficiency evolved from a broad distribution of low values to a mosaic distribution of multi-type zones. (3) After 2011, the ratio of CO2 footprint compensation amounts to local Gross Domestic Product (GDP) in most municipalities was less than 0.01%, with its center of gravity shifting cyclically. It was appropriate to start charging the CO2 footprint compensation amounts after 2011, with a dynamic adjustment of 3 years. (4) After 2007, the supply-demand relationship of CO2 trading continued to deteriorate, and it eased in 2016. However, its operational mechanism was still very fragile and highly dependent on a few pioneering municipalities. INNOVATIONS: In this study, we designed a horizontal CO2 compensation mechanism from the binary perspective of CO2 footprints and CO2 trading. In this mechanism, the former determines the CO2 footprint compensation amounts paid by each municipality based on whether the CO2 footprint exceeds its CO2 allowance. The latter determines the CO2 trading compensation amounts paid by the purchasing municipalities based on their CO2 emissions efficiency. This system balances equity and efficiency and provides new ideas for horizontal CO2 compensation.

The Heterogeneous Effects of Formal and Informal Environmental Regulation on Green Technology Innovation-An Empirical Study of 284 Cities in China.

Promoting green technology innovation (GTI) through environmental regulation is a key measure in reducing the severity of environmental problems. However, the effects of formal environmental regulation (FER) and informal environmental regulation (IER) on GTI have not been clarified. Through theoretical analysis, this paper analyzes the effects of FER and IER on GTI based on OLS and GTWR models. The results show the following: (1) In all Chinese cities, both FER and IER have had a positive impact on GTI. The impact of FER has been much stronger than that of IER. They show a linkage effect, and their interaction (TER) has had a positive impact on GTI. (2) In terms of spatial heterogeneity, the impact of FER, IER, and TER on GTI has decreased across the east-west gradient and has been supplemented by a core-periphery structure. (3) In terms of urban heterogeneity, the impact of FER, IER, and TER has decreased with the size of the city. This study has the potential to strengthen the effect of environmental regulation on GTI. It can provide a decision-making reference for cities to coordinate FER and IER strategies, and provides evidence for adopting regionally differentiated environmental regulation strategies.