Spatiotemporal trends of hemorrhagic fever with renal syndrome (HFRS) in China under climate variation.

Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic disease caused by the rodent-transmitted orthohantaviruses (HVs), with China possessing the most cases globally. The virus hosts in China are Apodemus agrarius and Rattus norvegicus, and the disease spread is strongly influenced by global climate dynamics. To assess and predict the spatiotemporal trends of HFRS from 2005 to 2098, we collected historical HFRS data in mainland China (2005-2020), historical and projected climate and population data (2005-2098), and spatial variables including biotic, environmental, topographical, and socioeconomic. Spatiotemporal predictions and mapping were conducted under 27 scenarios incorporating multiple integrated representative concentration pathway models and population scenarios. We identify the type of magistral HVs host species as the best spatial division, including four region categories. Seven extreme climate indices associated with temperature and precipitation have been pinpointed as key factors affecting the trends of HFRS. Our predictions indicate that annual HFRS cases will increase significantly in 62 of 356 cities in mainland China. Rattus regions are predicted to be the most active, surpassing Apodemus and Mixed regions. Eighty cities are identified as at severe risk level for HFRS, each with over 50 reported cases annually, including 22 new cities primarily located in East China and Rattus regions after 2020, while 6 others develop new risk. Our results suggest that the risk of HFRS will remain high through the end of this century, with Rattus norvegicus being the most active host, and that extreme climate indices are significant risk factors. Our findings can inform evidence-based policymaking regarding future risk of HFRS.

Enzyme-Responsive Supramolecular Self-Assembly in Small Amphiphiles.

Enzyme-responsive molecular assemblies have recently made remarkable progress, owing to their widespread applications. As a class of catalysts with high specificity and efficiency, enzymes play a critical role in producing new molecules and maintaining metabolic stability in living organisms. Therefore, the study of enzyme-responsive assembly aids in understanding the origin of life and the physiological processes occurring within living bodies, contributing to further advancements across various disciplines. In this Review, we summarize three kinds of enzyme-responsive assembly systems in amphiphiles: enzyme-triggered assembly, disassembly, and structural transformation. Furthermore, motivated by the fact that biological macromolecules and complex structures all originated with small molecules, our focus lies on the small amphiphiles (e.g., peptides, surfactants, fluorescent molecules, and drug molecules). We also provide an outlook on the potential of enzyme-responsive assembly systems for biomimetic development and hope this Review will attract more attention to this emerging research branch at the intersection of assembly chemistry and biological science.

Quantitative assessment of hyperperfusion using arterial spin labeling to predict hemorrhagic transformation in acute ischemic stroke patients with mechanical endovascular therapy.

OBJECTIVES: This study was aimed to quantitatively assess hyperperfusion using arterial spin labeling (ASL) to predict hemorrhagic transformation (HT) in acute ischemic stroke (AIS) patients. METHODS: This study enrolled 98 AIS patients with anterior circulation large vessel occlusion within 24 h of symptom onset. ASL was performed before mechanical endovascular therapy. On pre-treatment ASL maps, a region with relative cerebral blood flow (CBF) ≥ 1.4 was defined as an area of hyperperfusion. The maximum CBF (CBFmax) of hyperperfusion was calculated for each patient. A non-contrast CT scan was performed during the subacute phase for the evaluation of HT. Good clinical outcome was defined as a 90-day modified Rankin scale score of 0-2. RESULTS: The CBFmax of hyperperfusion (odds ratio, 1.023; 95% confidence interval [CI], 1.005-1.042; p = 0.012) was an independent risk factor for the status of HT. The CBFmax of hyperperfusion for HT showed an area under the curve of 0.735 (95% CI, 0.588-0.882) with optimal cutoff value, sensitivity, and specificity being 146.5 mL/100 g/min, 76.9%, and 69.6%, respectively. There was a statistically significant relationship between HT grades (from no HT to PH2) and CBFmax of hyperperfusion with a Spearman rank correlation of 0.446 (p = 0.001). In addition, low CBFmax of hyperperfusion were associated with good functional outcome (95% CI, 17.130-73.910; p = 0.002). CONCLUSIONS: High CBFmax of hyperperfusion was independently associated with subsequent HT and low CBFmax of hyperperfusion linked to good functional outcome. There was a positive correlation between HT grade and CBFmax. CLINICAL RELEVANCE STATEMENT: Arterial spin labeling is a noninvasive and contrast agent-independent technique, which is sensitive in detecting hyperperfusion. This study shows that the cerebral blood flow of hyperperfusion is associated with clinical prognosis, which will benefit more patients. KEY POINTS: • Quantitative assessment of hyperperfusion using pre-treatment arterial spin labeling to predict hemorrhagic transformation and prognosis in acute ischemic stroke patients. • The maximum cerebral blood flow of hyperperfusion was associated with hemorrhagic transformation and clinical prognosis and higher maximum cerebral blood flow of hyperperfusion was associated with higher grade hemorrhagic transformation. • The maximum cerebral blood flow of hyperperfusion can predict hemorrhagic transformation which enables timely intervention to prevent parenchymal hematoma.

Projection of Mortality Burden Attributable to Nonoptimum Temperature with High Spatial Resolution in China.

The updated climate models provide projections at a fine scale, allowing us to estimate health risks due to future warming after accounting for spatial heterogeneity. Here, we utilized an ensemble of high-resolution (25 km) climate simulations and nationwide mortality data from 306 Chinese cities to estimate death anomalies attributable to future warming. Historical estimation (1986-2014) reveals that about 15.5% [95% empirical confidence interval (eCI):13.1%, 17.6%] of deaths are attributable to nonoptimal temperature, of which heat and cold corresponded to attributable fractions of 4.1% (eCI:2.4%, 5.5%) and 11.4% (eCI:10.7%, 12.1%), respectively. Under three climate scenarios (SSP126, SSP245, and SSP585), the national average temperature was projected to increase by 1.45, 2.57, and 4.98 °C by the 2090s, respectively. The corresponding mortality fractions attributable to heat would be 6.5% (eCI:5.2%, 7.7%), 7.9% (eCI:6.3%, 9.4%), and 11.4% (eCI:9.2%, 13.3%). More than half of the attributable deaths due to future warming would occur in north China and cardiovascular mortality would increase more drastically than respiratory mortality. Our study shows that the increased heat-attributable mortality burden would outweigh the decreased cold-attributable burden even under a moderate climate change scenario across China. The results are helpful for national or local policymakers to better address the challenges of future warming.

Achalasia Following SARS-CoV-2 Infection: A Case Report and Review of Literature.

RATIONALE: The COVID-19 pandemic caused by the SARS-CoV-2 virus has led to health complications beyond respiratory symptoms, revealing multi-organ involvement, including potential gastrointestinal implications. PATIENT CONCERNS: We present a case of a 40-year-old female without any history of achalasia who developed symptoms of the condition following a confirmed SARS-CoV-2 infection. Unusually, multiple esophageal ulcers were identified, which are not typically associated with achalasia. DIAGNOSIS: Achalasia and esophageal ulcers were confirmed through a series of examinations, including barium swallow, CT scan, and upper endoscopy. Furthermore, immunohistochemical staining of esophageal biopsy specimens revealed the presence of the SARS-CoV-2 spike protein, suggesting direct viral involvement. INTERVENTIONS: The patient was treated with calcium channel blockers and proton pump inhibitors and later underwent a peroral endoscopic myotomy (POEM) procedure following the resolution of her COVID-19 infection. OUTCOME: After the POEM procedure, the patient made a good recovery. LESSONS: This case underscores the potential for SARS-CoV-2 to trigger gastrointestinal complications and emphasizes the need for ongoing patient management and further research into the long-term implications of COVID-19. Despite the single-case nature of this report, it contributes to the expanding understanding of the diverse and multi-systemic impact of COVID-19.

Formation of Size-Controllable Tetragonal Nanoprisms by Crystallization-Directed Ionic Self-Assembly of Anionic Porphyrin and PEO-Containing Triblock Cationic Copolymer.

The creation of anisotropic nanostructures with precise size control is desirable for new properties and functions, but it is challenging for ionic self-assembly (ISA) because of the non-directional electrostatic interactions. Herein, the formation of size-controllable tetragonal nanoprisms is reported via crystallization-directed ionic self-assembly (CDISA) through evaporating a micellar solution on solid substrates. First, ISA is designed with a crystalline polyethylene oxide (PEO) containing cationic polymer poly(2-(2-guanidinoethoxy)ethyl methacrylate)-b-poly(ethyleneoxide)-b-poly(2-(2-guanidinoethoxy)-ethylmethacrylate) (PGn -PEO230 -PGn ) and an anionic 5,10,15,20-Tetrakis(4-sulfonatophenyl) porphyrin (TPPS) to form micelles in aqueous solution. The PG segments binds excessive TPPS with amplenet chargeto form hydrophilic corona, while the PEO segments are unprecedentedly dehydrated and tightly packed into cores. Upon naturally drying the micellar solution on a silicon wafer, PEO crystallizationdirects the micelles to aggregate into square nanoplates, which are further connected to nanoprisms. Length and width of the nanoprisms can be facilely tuned by varying the initial concentration. In this hierarchical process, the aqueous self-assembly is prerequisite and the water evaporation rate is crucial for the formation of nanostructures, which provides multiple factors for morphology regulating. Such precise size-control strategy is highly expected to provide a new vision for the design of advanced materials with size controllable anisotropic nanostructures.

The efficacy of low-frequency ultrasound as an added treatment for chronic wounds: A meta-analysis.

We performed a meta-analysis to evaluate the effect of low-frequency ultrasound as an added treatment for chronic wounds. A systematic literature search up to May 2022 was performed and 838 subjects with chronic wounds at the baseline of the studies; 412 of them were using the low-frequency ultrasound (225 low-frequency high-intensity contact ultrasound for diabetic foot wound ulcers, and 187 low-frequency low-intensity non-contact ultrasound for a venous leg wound ulcers), and 426 were using standard care (233 sharp debridements for diabetic foot wound ulcers and 193 sham treatments for venous leg wound ulcers). Odds ratio (OR), and mean difference (MD) with 95% confidence intervals (CIs) were calculated to assess the effect of low-frequency ultrasound as an added treatment for chronic wounds using the dichotomous, and contentious methods with a random or fixed-effect model. The low-frequency high-intensity contact ultrasound for diabetic foot wound ulcers had significantly lower non-healed diabetic foot wound ulcers at ≥3 months (OR, 0.37; 95% CI, 0.24-0.56, P < .001), a higher percentage of diabetic foot wound ulcers area reduction (MD, 17.18; 95% CI, 6.62-27.85, P = .002) compared with sharp debridement for diabetic foot wound ulcers. The low-frequency low-intensity non-contact ultrasound for a venous leg wound ulcers had a significantly lower non-healed venous leg wound ulcers at ≥3 months (OR, 0.31; 95% CI, 0.15-0.62, P = .001), and higher percentage venous leg wound ulcers area reduction (MD, 18.96; 95% CI, 2.36-35.57, P = .03) compared with sham treatments for a venous leg wound ulcers. The low-frequency ultrasound as an added treatment for diabetic foot wound ulcers and venous leg wound ulcers had significantly lower non-healed chronic wound ulcers at ≥3 months, a higher percentage of chronic wound ulcers area reduction compared with standard care. The analysis of outcomes should be with caution because of the low sample size of all the 17 studies in the meta-analysis and a low number of studies in certain comparisons.

IVIM using convolutional neural networks predicts microvascular invasion in HCC.

OBJECTIVES: The study aimed to investigate the diagnostic performance of intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging for prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) using convolutional neural networks (CNNs). METHODS: This retrospective study included 114 patients with pathologically confirmed HCC from December 2014 to August 2021. All patients underwent MRI examination including IVIM sequence with 9 b-values preoperatively. First, 9 b-value images were superimposed in the channel dimension, and a b-value volume with a shape of 32 × 32 × 9 dimension was obtained. Secondly, an image resampling method was performed for data augmentation to generate more samples for training. Finally, deep features to predict MVI in HCC were directly derived from a b-value volume based on the CNN. Moreover, a deep learning model based on parameter maps and a fusion model combined with deep features of IVIM, clinical characteristics, and IVIM parameters were also constructed. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic performance for MVI prediction in HCC. RESULTS: Deep features directly extracted from IVIM-DWI (0.810 (range 0.760, 0.829)) using CNN yielded better performance for prediction of MVI than those from IVIM parameter maps (0.590 (range 0.555, 0.643)). Furthermore, the performance of the fusion model combined with deep features of IVIM-DWI, clinical features (α-fetoprotein (AFP) level and tumor size), and apparent diffusion coefficient (ADC) (0.829 (range 0.776, 0.848)) was slightly improved. CONCLUSIONS: Deep learning with CNN based on IVIM-DWI can be conducive to preoperative prediction of MVI in patients with HCC. KEY POINTS: • Deep learning assessment of IVIM data for prediction of MVI in HCC can overcome the unstable and low performance of IVIM parameters. • Deep learning model based on IVIM performs better than parameter values, clinical features, and deep learning model based on parameter maps. • The fusion model combined with deep features of IVIM, clinical characteristics, and ADC yields better performance for prediction of MVI than the model only based on IVIM.

A fully automatic artificial intelligence-based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis.

OBJECTIVES: An accurate and rapid diagnosis is crucial for the appropriate treatment of pulmonary tuberculosis (TB). This study aims to develop an artificial intelligence (AI)-based fully automated CT image analysis system for detection, diagnosis, and burden quantification of pulmonary TB. METHODS: From December 2007 to September 2020, 892 chest CT scans from pathogen-confirmed TB patients were retrospectively included. A deep learning-based cascading framework was connected to create a processing pipeline. For training and validation of the model, 1921 lesions were manually labeled, classified according to six categories of critical imaging features, and visually scored regarding lesion involvement as the ground truth. A "TB score" was calculated based on a network-activation map to quantitively assess the disease burden. Independent testing datasets from two additional hospitals (dataset 2, n = 99; dataset 3, n = 86) and the NIH TB Portals (n = 171) were used to externally validate the performance of the AI model. RESULTS: CT scans of 526 participants (mean age, 48.5 ± 16.5 years; 206 women) were analyzed. The lung lesion detection subsystem yielded a mean average precision of the validation cohort of 0.68. The overall classification accuracy of six pulmonary critical imaging findings indicative of TB of the independent datasets was 81.08-91.05%. A moderate to strong correlation was demonstrated between the AI model-quantified TB score and the radiologist-estimated CT score. CONCLUSIONS: The proposed end-to-end AI system based on chest CT can achieve human-level diagnostic performance for early detection and optimal clinical management of patients with pulmonary TB. KEY POINTS: • Deep learning allows automatic detection, diagnosis, and evaluation of pulmonary tuberculosis. • Artificial intelligence helps clinicians to assess patients with tuberculosis. • Pulmonary tuberculosis disease activity and treatment management can be improved.

Cyclodextrin-catalyzed self-assembly of a coordinating fluorescent molecule into microflowers.

We report that γ-cyclodextrin (γ-CD) is able to catalyze the self-assembly process of the coordinating fluorescent molecule pyrenebutyrate with Zn2+. The direct interaction between pyrenebutyrate and Zn2+ would simply lead to amorphous precipitates, whereas addition of Zn2+ to the host-guest complex of pyrenebutyrate @ γ-CD would generate well-defined microflowers that have exactly the same composition as the amorphous pyrenebutyrate/Zn2+. The evidence of host-guest formation between 1-PBA and γ-CD and the absence of γ-CD in the final microflowers manifest that γ-CD acts as a catalyst in the self-assembly process. We envision that this dynamic host-guest chemistry would be very promising in creating catassemblies.