Design and preparation of naringenin loaded functional biomimetic nano-drug delivery system for Alzheimer's disease

Efficient brain drug delivery has been a challenge in the treatment of Alzheimer's Disease and other brain disorders as blood-brain barrier (BBB) impedes most drugs to reach brain. To overcome this obstacle, we developed a novel TGN decorated erythrocyte membrane-coated poly (lactic-co-glycolic acid) nanoparticle (TRNNs). The nanoparticle significantly boosted the penetration (7.3 times) in a U-118MG and HCMEC/D3 cell co-culture BBB model in vitro. Living image was performed to assess the TRNNs distribution in vivo. The fluorescence intensity in the isolated brain of TRDNs-treated mice was about 8 times that of the DNs-treated. In the novel object recognition test, the mice after administration of TRDNs showed higher recognition index (0.414 ± 0.016) than the model group (0.275 ± 0.019). A significant increase in the number of dendritic spines from TRNNs administrated mice hippocampi neurons was observed after Golgi stain. This improvement of neurons was also confirmed by the significant high expression of PSD95 protein level in hippocampi. We measured the OD values of Aβ25-35 induced PC12 cells that pre-treatment with different nanoparticles and concluded that TRNNs had a robust neuroprotection effect. Above all, functional biomimetic nanoparticles could increase the accumulation of naringenin into brain, thereby enable the drug to exert greater therapeutic effects.

Profiles in neglect of older adult care workers in a long-term care facility: a latent profile analysis

Background

Neglect is a common form of abuse, and long-term care facilities record higher incidences of this abuse. Given that older adult care workers are the main workforce in these facilities, their neglectful behavior requires public health attention. Internal individual characteristics can lead to older adult abuse, and managing workers who abuse older adults may require various methods. This study aimed to identify the profiles of neglect among older adult care workers in long-term care facilities and explore the influencing factors of neglect.

Methods

In this cross-sectional study, a convenience sample of older adult care workers from 15 long-term care facilities in Shandong Province (N = 421) completed a questionnaire on the characteristics associated with neglect. Latent profile analysis was used to identify distinct neglect profiles and promote the understanding of individual characteristics associated with varying levels of neglect. One-way analysis of variance and multivariate logistic regression analyses were used to examine the population characteristic differences.

Results

Older adult care workers exhibited three neglect profiles, namely, the "low-risk group," "medium-risk group," and "high-risk group." Males, participants with no employment qualification certificate, and those who did not attend regular training represented the majority of those in the "high-risk group." Participants with a monthly income of more than ¥ 4,000 and nursing 1-2 older adults simultaneously represented the majority of those in the "low-risk group."

Conclusion

Long-term care facility administrators should tailor interventions to individual care worker profiles to reduce neglect behaviors and improve care levels.

Autophagy core protein BECN1 is vital for spermatogenesis and male fertility in mice†

Mammalian spermatogenesis is a highly complex multi-step biological process, and autophagy has been demonstrated to be involved in the process of spermatogenesis. Beclin-1/BECN1, a core autophagy factor, plays a critical role in many biological processes and diseases. However, its function in spermatogenesis remains largely unclear. In the present study, germ cell-specific Beclin 1 (Becn1) knockout mice were generated and were conducted to determine the role of Becn1 in spermatogenesis and fertility of mice. Results indicate that Becn1 deficiency leads to reduced sperm motility and quantity, partial failure of spermiation, actin network disruption, excessive residual cytoplasm, acrosome malformation, and aberrant mitochondrial accumulation of sperm, ultimately resulting in reduced fertility in male mice. Furthermore, inhibition of autophagy was observed in the testes of germ cell-specific Becn1 knockout mice, which may contribute to impaired spermiogenesis and reduced fertility. Collectively, our results reveal that Becn1 is essential for fertility and spermiogenesis in mice.

Improved small vessel visibility in diabetic foot arteriography using dual-energy CT

AIM

To test the feasibility and performance of dual-energy computed tomography (DECT) in foot arteriography of diabetic patients, where contrast medium is largely reduced within the small vessels.

MATERIALS AND METHODS

A total of 50 diabetic patients were enrolled prospectively, where DECT was acquired immediately after the CT angiography (CTA, group A) of the lower extremity. Two images were derived from the DECT data, one optimal virtual monochromatic image (VMI, group B) and one fusion image (group C), both of which were compared against the CTA image for visualising the foot arteries. The contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were evaluated. The arterial course and contrast were graded each using a five-point scale. The clarity of small vessel depiction was quantified by comparing the number of plantar metatarsal arteries found in the maximum intensity projection image.

RESULTS

The median CNRs and SNRs obtained in group B were approximately 45% and 20% higher than those in groups A and C, respectively (p<0.05). Group B also received higher subjective scores on the posterior tibial artery and the foot arteries (all >3) than groups A and C. The number of visible branches of the plantar metatarsal arteries was found to be substantially higher (p<0.05) in group B (median=6) than in groups A (median=2) and C (median=4).

CONCLUSION

DECT was found to be superior to conventional CTA in foot arteriography, and beyond the lower extremity, it might be a general favourable solution for imaging regions with small vessels and reduced contrast medium.

Estimating Functional Brain Networks by Low-Rank Representation With Local Constraint

The functional architecture undergoes alterations during the preclinical phase of Alzheimer's disease. Consequently, the primary research focus has shifted towards identifying Alzheimer's disease and its early stages by constructing a functional connectivity network based on resting-state fMRI data. Recent investigations show that as Alzheimer's Disease (AD) progresses, modular tissue and connections in the core brain areas of AD patients diminish. Sparse learning methods are powerful tools for understanding Functional Brain Networks (FBNs) with Regions of Interest (ROIs) and a connectivity matrix measuring functional coherence between them. However, these tools often focus exclusively on functional connectivity measures, neglecting the brain network's modularity. Modularity orchestrates dynamic activities within the FBN to execute intricate cognitive tasks. To provide a comprehensive delineation of the FBN, we propose a local similarity-constrained low-rank sparse representation (LSLRSR) method that encodes modularity information under a manifold-regularized network learning framework and further formulate it as a low-rank sparse graph learning problem, which can be solved by an efficient optimization algorithm. Specifically, for each modularity structure, the Schatten p-norm regularizer reduces the reconstruction error and provides a better approximation of the low-rank constraint. Furthermore, we adopt a manifold-regularized local similarity prior to infer the intricate relationship between subnetwork similarity and modularity, guiding the modeling of FBN. Additionally, the proximal average method approximates the joint solution's proximal map, and the resulting nonconvex optimization problems are solved using the alternating direction multiplier method (ADMM). Compared to state-of-the-art methods for constructing FBNs, our algorithm generates a more modular FBN. This lays the groundwork for further research into alterations in brain network modularity resulting from diseases.

Nonfrontal Control of Working Memory

Items held in visual working memory can be quickly updated, replaced, removed, and even manipulated in accordance with current behavioral goals. Here, we use multivariate pattern analyses to identify the patterns of neuronal activity that realize the executive control processes supervising these flexible stores. We find that portions of the middle temporal gyrus and the intraparietal sulcus represent what item is cued for continued memorization independently of representations of the item itself. Importantly, this selection-specific activity could not be explained by sensory representations of the cue and is only present when control is exerted. Our results suggest that the selection of memorized items might be controlled in a distributed and decentralized fashion. This evidence provides an alternative perspective to the notion of "domain general" central executive control over memory function.

Erythrocyte membrane-coated nanocarriers modified by TGN for Alzheimer's disease

Alzheimer's disease (AD) is an aging-related neurodegenerative disease, and the main pathological feature was β-amyloid protein (Aβ) deposition. Recently, bioactive materials-based drug delivery system has been widely investigated for the treatment of AD. In this study, we developed a red blood cells (RBC) membrane-coated polycaprolactone (PCL) nanoparticles (NPs) loading with a therapeutic agent for AD, curcumin (Cur). A functional peptide TGNYKALHPHN (TGN) was conjugated to the surface of membrane for blood-brain barrier (BBB) transport (TGN-RBC-NPs-Cur). TGN peptide can be recognized by receptors on the BBB and has great potential for brain transport. To confirm the targeted delivery of Cur to the brain, a cell co-culturing immortalized human cerebral microvascular endothelial cells and human brain astrocytes glioblastoma (hCMEC/D3 and U-118MG) in vitro model was established. As a result, the BBB transporting ratio of TGN-RBC-NPs-FITC was 29.64% at 12 h which was approximately eight-fold than RBC-NPs-FITC. The improvement of drug accumulation in the AD lesion was confirmed by the NPs modified with the BBB-penetrating peptide in the fluorescence imaging and quantitative analysis with UPLC-MS/MS in vivo. The neuroprotective effects were evaluated with new object recognition behavioral test, in vitro AD cell model, dendritic spine stain, GFAP and IBA1 immunofluorescence stain. The spatial learning and memory abilities of the AD model mice treated with TGN-RBC-NPs-Cur were obviously enhanced compared with the AD control mice and were also better than Cur at the same dosage. These results were consistent with the values of protection index of rat adrenal pheochromocytoma cells (PC12 cells) treated by Aβ25-35. TGN-RBC-NPs-Cur increased the dendritic segments densities and restrained activation of microglia and astrocytes of AD mice, as well as reversed cognitive function of AD mice. All of the results demonstrated TGN-RBC-NPs-Cur a promising therapeutic strategy for delaying the progression of AD by designing biomimetic nanosystems to deliver drugs into the brain.

Observation and Characterization of Vibrationally Active Surface Species Accessed with Nonthermal Nitrogen Plasmas

Polycrystalline Ni, Pd, Cu, Ag, and Au foils exposed to nonthermal plasma (NTP)-activated N2 are found to exhibit a vibrational feature near 2200 cm-1 in polarization-modulation infrared reflection-absorption spectroscopy (PM-IRAS) observations that are not present in the same materials exposed to N2 under nonplasma conditions. The feature is similar to that reported elsewhere and is typically assigned to chemisorbed N2. We employ a combination of temperature-dependent experiments, sequential dosing, X-ray photoelectron spectroscopy, isotopic labeling, and density functional theory calculations to characterize the feature. Results are most consistent with a triatomic species, likely NCO, with the C and O likely originating from ppm-level impurities in the ultrahigh-purity (UHP) Ar and/or N2 gas cylinders. The work highlights the potential for nonthermal plasmas to access adsorbates inaccessible thermally as well as the potential contributions of ppm-level impurities to corrupt the interpretation of plasma catalytic chemistry.

Multidimensional Widefield Infrared-Encoded Spontaneous Emission Microscopy: Distinguishing Chromophores by Ultrashort Infrared Pulses

Photoluminescence (PL) imaging has broad applications in visualizing biological activities, detecting chemical species, and characterizing materials. However, the chemical information encoded in the PL images is often limited by the overlapping emission spectra of chromophores. Here, we report a PL microscopy based on the nonlinear interactions between mid-infrared and visible excitations on matters, which we termed MultiDimensional Widefield Infrared-encoded Spontaneous Emission (MD-WISE) microscopy. MD-WISE microscopy can distinguish chromophores that possess nearly identical emission spectra via conditions in a multidimensional space formed by three independent variables: the temporal delay between the infrared and the visible pulses (t), the wavelength of visible pulses (λvis), and the frequencies of the infrared pulses (ωIR). This method is enabled by two mechanisms: (1) modulating the optical absorption cross sections of molecular dyes by exciting specific vibrational functional groups and (2) reducing the PL quantum yield of semiconductor nanocrystals, which was achieved through strong field ionization of excitons. Importantly, MD-WISE microscopy operates under widefield imaging conditions with a field of view of tens of microns, other than the confocal configuration adopted by most nonlinear optical microscopies, which require focusing the optical beams tightly. By demonstrating the capacity of registering multidimensional information into PL images, MD-WISE microscopy has the potential of expanding the number of species and processes that can be simultaneously tracked in high-speed widefield imaging applications.

Eco-friendly microalgae harvesting using lipid-cored particles with a comparative life-cycle assessment

This study proposed an innovative approach using lipid-cored particles (LCPs) aimed at addressing the efficiency, cost, and environmental impact challenges in microalgae harvesting. Cetyltrimethylammonium bromide (CTAB) and chitosan (CS) were used to modify LCPs and to optimize efficiency and investigate the mechanisms of harvesting with Chlorella vulgaris. Results showed that a maximum harvesting efficiency of 97.14 % was achieved using CS-LCPs. Zeta potential and microscopic images revealed the presence of embedded CS-LCPs within microalgal flocs. Fractal dimension data suggested looser aggregates of CS-LCPs and Chlorella vulgaris, corroborated by Excitation-emission matrices (EEM) analysis further confirmation the presence of bridging networks. Moreover, life cycle assessment of five harvesting methods pointed freshwater ecotoxicity potential (FEP) and terrestrial ecotoxicity potential (TEP) as major environmental impacts, mainly from flocculant use, carrier production, and electricity consumption. Notably, LCPs showed the lowest global warming potential (GWP) at 1.54 kg CO2 eq, offering a viable, low-carbon, cost-effective harvesting alternative.

Cd-Free Pure Sulfide Kesterite Cu2 ZnSnS4 Solar Cell with Over 800 mV Open-Circuit Voltage Enabled by Phase Evolution Intervention

The Cd-free Cu2 ZnSnS4 (CZTS) solar cell is an ideal candidate for producing low-cost clean energy through green materials owing to its inherent environmental friendliness and earth abundance. Nevertheless, sulfide CZTS has long suffered from severe open-circuit voltage (VOC ) deficits, limiting the full exploitation of performance potential and further progress. Here, an effective strategy is proposed to alleviate the nonradiative VOC loss by manipulating the phase evolution during the critical kesterite phase formation stage. With a Ge cap layer on the precursor, premature CZTS grain formation is suppressed at low temperatures, leading to fewer nucleation centers at the initial crystallization stage. Consequently, the CZTS grain formation and crystallization are deferred to high temperatures, resulting in enhanced grain interior quality and less unfavorable grain boundaries in the final film. As a result, a champion efficiency of 10.7% for Cd-free CZTS solar cells with remarkably high VOC beyond 800 mV (63.2% Schockley-Queisser limit) is realized, indicating that nonradiative recombination is effectively inhibited. This strategy may advance other compound semiconductors seeking high-quality crystallization.

Thirteen-year viral suppression and immunologic recovery of LPV/r-based regimens in pediatric HIV treatment: a multicenter cohort study in resource-constrained settings of China

Background

Antiretroviral Therapy (ART) in children remains challenging due to resource-constrained settings. We conducted a 13-year, prospective, multicenter cohort study on the effectiveness and safety of LPV/r-based regimens in ART-naive and ART-experienced children.

Methods

From January 2008 to May 2021, children living with HIV-1 were recruited with LPV/r-based regimens from 8 clinical research sites in 6 provinces in China. Effectiveness outcomes were virologic failure (defined as at least two consecutive measurements of VL > 200 copies/mL after 6 months of ART) and immune response (defined as CD4% recovered to more than 25% after 12 months of treatment). The safety outcomes were treatment-related grade 2-4 adverse events and abnormal laboratory test results.

Results

A total of 345 ART-naïve children and 113 ART-experienced children were included in this cohort study. The median follow-up time was 7.3 (IQR 5.5-10.5) years. The incidence density of virologic failure was 4.1 (95% CI 3.3-4.9) per 100 person-years in ART-naïve children and 5.0 (95% CI 3.5-6.5) per 100 person-years in ART-experienced children. Kaplan Meyer (KM) curve analysis showed children with ART experience were at a higher risk of virologic failure (p < 0.05). The risk factors of virologic failure in ART-naïve children were clinic setting in rural hospitals (aHR = 2.251, 1.108-4.575), annual missed dose times >5 days of LPV intake (aHR = 1.889, 1.004-3.554); The risk factor of virologic failure in ART-experienced children was missed dose times >5 days (aHR = 2.689, 1.299-5.604) and mother as caregivers for ART administration (aHR = 0.475, 0.238-0.948). However, during long-term treatment, viral suppression rates between ART-naïve and ART-experienced children remained similar. No significant differences were observed in the immune response, treatment-related grade 2-4 events, and abnormal laboratory test results between ART-naïve children and ART-experienced children.

Conclusion

Our research underscores that with consistent, long-term treatment of LPV/r-based regimens, ART-experienced children can achieve therapeutic outcomes comparable to ART-naïve children. It provides crucial insights on LPV/r-based regimens in pediatric HIV treatment, especially in resource-limited settings where high-cost Integrase Strand Transfer Inhibitors (INSTs) are inaccessible. This evidence-based understanding provides an essential addition to the global therapeutic strategies for pediatric HIV treatment.

[Promote the high-quality development of gastrointestinal surgery with technological concept innovation]

In the past 30 years, gastrointestinal surgery in China has made significant progress, which is reflected in the gradual standardization of clinical diagnosis and treatment, significant improvement in surgical quality, improvement in short-term and long-term postoperative outcomes, and continuous development of high-quality clinical research. At present, the spectrum of disease in gastrointestinal surgery has changed from traditional benign diseases to malignant diseases represented by gastric cancer and colorectal cancer, metabolic diseases represented by obesity and diabetes, and immune diseases represented by inflammatory bowel disease. It is necessary to carry out full-cycle management for patients. In the new era full of opportunities and challenges, surgeons must be driven by innovation in surgical technology, guided by high-quality clinical research and guaranteed by standardized diagnosis and treatment of diseases, to continue to promote the high-quality development of gastrointestinal surgery in China.

A composite electrodynamic mechanism to reconcile spatiotemporally resolved exciton transport in quantum dot superlattices

Quantum dot (QD) solids are promising optoelectronic materials; further advancing their device functionality requires understanding their energy transport mechanisms. The commonly invoked near-field Förster resonance energy transfer (FRET) theory often underestimates the exciton hopping rate in QD solids, yet no consensus exists on the underlying cause. In response, we use time-resolved ultrafast stimulated emission depletion (STED) microscopy, an ultrafast transformation of STED to spatiotemporally resolve exciton diffusion in tellurium-doped cadmium selenide-core/cadmium sulfide-shell QD superlattices. We measure the concomitant time-resolved exciton energy decay due to excitons sampling a heterogeneous energetic landscape within the superlattice. The heterogeneity is quantified by single-particle emission spectroscopy. This powerful multimodal set of observables provides sufficient constraints on a kinetic Monte Carlo simulation of exciton transport to elucidate a composite transport mechanism that includes both near-field FRET and previously neglected far-field emission/reabsorption contributions. Uncovering this mechanism offers a much-needed unified framework in which to characterize transport in QD solids and additional principles for device design.

Development of A Nomogram for Progression-free Survival in Patients with Stage II/T3N0 Nasopharyngeal Carcinoma to Explore Different Treatment Modalities

Purpose To explore the prognostic value of clinical and serological risk factors for progression-free survival (PFS) in stage II and T3N0 nasopharyngeal carcinoma (NPC) and construct a nomogram based on these factors. Additionally, to investigate the long-term survival and short-term toxic reactions of patients in different risk stratification under different treatment modalities. Methods The patients were randomly divided into training and validation cohorts in a 7:3 ratio. Independent prognostic factors were identified using Cox regression analysis, and a nomogram was constructed by combining these predictive factors with the TNM staging system. The nomogram was then validated in the validation cohort, and patients were classified into different risk groups based on the nomogram. The PFS, overall survival (OS), and acute toxicities were compared among different treatment modalities after balancing baseline characteristics. Results Multivariate Cox regression analysis indicated that pathological type, alkaline phosphatase (ALP) and lactate dehydrogenase (LDH) were independent prognostic factors(p<0.05) in this study. The nomogram showed good prognostic accuracy in both the training and validation cohorts (C-index of 0.73 and 0.70, respectively). In the different risk subgroups, there were no statistically significant differences in PFS and OS between radiotherapy and chemoradiotherapy groups(p>0.05). The treatment modality of combined chemotherapy was associated with more acute toxic reactions. Conclusion We established and validated a nomogram for predicting PFS in patients with stage II/T3N0 NPC. Intensity-modulated radiation therapy (IMRT) combined with chemotherapy did not provide additional survival benefits for these patients and was associated with more chemotherapy-related side effects.

Dosimetric Quality of Artificial Intelligence Based Organ at Risk Segmentation

PURPOSE/OBJECTIVE(S)

to compare dosimetric parameters between Artificial intelligence (AI) generated organ at risks (OAR) and Radiation Oncologist approved OARs and evaluation of appropriateness unedited AI- OARs in routine clinical plan optimization and evaluation.

MATERIALS/METHODS

The OARs (lung, spinal cord and heart) for twenty SBRT (stereotactic body radiotherapy) lung CT simulation datasets were derived by AI based segmentation algorithms. These AI- OARs were edited by a staff Radiation Oncologist and then subjected to our SBRT peer-review process at our institution. A SBRT plan based on the approved contours was created. Dosimetric parameters for the unedited AI-OARs and edited physician-approved OARs were then compared.

RESULTS

Lung V20 differences between AI- OAR and physician- OAR varied from 0.01% - 0.7% with a mean value of 0.1% difference (p-value 0.004). Spinal cord D0.03cc varied from 0.02 Gy - 0.9 Gy with a mean value of 0.3 Gy difference (p-value 0.002). Heart D0.03cc varied from 0.01 Gy - 4.3 Gy with mean value 0.9 Gy difference (p-value 0.02).

CONCLUSION

Dosimetric parameters for AI-based lung, spinal cord and heart OARs vs physician approved OARs were different, overall, the differences were generally small. These differences are likely on par with inter-observer differences seen between individual radiation oncologists. Unedited OARs have the promise for routine use in plan optimization and evaluation to further improve efficiency.

Evaluation of skin sympathetic nervous activity for classification of intracerebral hemorrhage and outcome prediction

Classification and outcome prediction of intracerebral hemorrhage (ICH) is critical for improving the survival rate of patients. Early or delayed neurological deterioration is common in ICH patients, which may lead to changes in the autonomic nervous system (ANS). Therefore, we proposed a new framework for ICH classification and outcome prediction based on skin sympathetic nervous activity (SKNA) signals. A customized measurement device presented in our previous papers was used to collect data. 117 subjects (50 healthy control subjects and 67 ICH patients) were recruited for this study to obtain their 5-min electrocardiogram (ECG) and SKNA signals. We extracted the signal's time-domain, frequency-domain, and nonlinear features and analyzed their differences between healthy control subjects and ICH patients. Subsequently, we established the ICH classification and outcome evaluation model based on the eXtreme Gradient Boosting (XGBoost). In addition, heart rate variability (HRV) as an ANS assessment method was also included as a comparison method in this study. The results showed significant differences in most features of the SKNA signal between healthy control subjects and ICH patients. The ICH patients with good outcomes have a higher change rate and complexity of SKNA signal than those with bad outcomes. In addition, the accuracy of the model for ICH classification and outcome prediction based on the SKNA signal was more than 91% and 83%, respectively. The ICH classification and outcome prediction based on the SKNA signal proved to be a feasible method in this study. Furthermore, the features of change rate and complexity, such as entropy measures, can be used to characterize the difference in SKNA signals of different groups. The method can potentially provide a new tool for rapid classification and outcome prediction of ICH patients. Index Terms-intracerebral hemorrhage (ICH), skin sympathetic nervous activity (SKNA), classification, outcome prediction, cardiovascular and cerebrovascular diseases.

MOF-based stimuli-responsive controlled release nanopesticide: mini review

By releasing an adequate amount of active ingredients when triggered by environmental and biological factors, the nanopesticides that respond to stimuli can enhance the efficacy of pesticides and contribute to the betterment of both the environment and food safety. The versatile nature and highly porous structure of metal-organic frameworks (MOFs) have recently garnered significant interest as drug carriers for various applications. In recent years, there has been significant progress in the development of metal-organic frameworks as nanocarriers for pesticide applications. This review focuses on the advancements, challenges, and potential future enhancements in the design of metal-organic frameworks as nanocarriers in the field of pesticides. We explore the various stimuli-responsive metal-organic frameworks carriers, particularly focusing on zeolitic imidazolate framework-8 (ZIF-8), which have been successfully activated by external stimuli such as pH-responsive or multiple stimuli-responsive mechanisms. In conclusion, this paper presents the existing issues and future prospects of metal-organic frameworks-based nanopesticides with stimuli-responsive controlled release.

Cadmium-Free Kesterite Thin-Film Solar Cells with High Efficiency Approaching 12

Cadmium sulfide (CdS) buffer layer is commonly used in Kesterite Cu2 ZnSn(S,Se)4 (CZTSSe) thin film solar cells. However, the toxicity of Cadmium (Cd) and perilous waste, which is generated during the deposition process (chemical bath deposition), and the narrow bandgap (≈2.4 eV) of CdS restrict its large-scale future application. Herein, the atomic layer deposition (ALD) method is proposed to deposit zinc-tin-oxide (ZTO) as a buffer layer in Ag-doped CZTSSe solar cells. It is found that the ZTO buffer layer improves the band alignment at the Ag-CZTSSe/ZTO heterojunction interface. The smaller contact potential difference of the ZTO facilitates the extraction of charge carriers and promotes carrier transport. The better p-n junction quality helps to improve the open-circuit voltage (VOC ) and fill factor (FF). Meanwhile, the wider bandgap of ZTO assists to transfer more photons to the CZTSSe absorber, and more photocarriers are generated thus improving short-circuit current density (Jsc). Ultimately, Ag-CZTSSe/ZTO device with 10 nm thick ZTO layer and 5:1 (Zn:Sn) ratio, Sn/(Sn + Zn): 0.28 deliver a superior power conversion efficiency (PCE) of 11.8%. As far as it is known that 11.8% is the highest efficiency among Cd-free kesterite thin film solar cells.

Sleep staging based on single-channel EEG and EOG with Tiny U-Net

Nowadays, many sleep staging algorithms have not been widely used in practical situations due to the lack of persuasiveness of generalization outside the given datasets. Thus, to improve generalization, we select seven highly heterogeneous datasets covering 9970 records with over 20k hours among 7226 subjects spanning 950 days for training, validation, and evaluation. In this paper, we propose an automatic sleep staging architecture called TinyUStaging using single-lead EEG and EOG. The TinyUStaging is a lightweight U-Net with multiple attention modules to perform adaptive recalibration of the features, including Channel and Spatial Joint Attention (CSJA) block and Squeeze and Excitation (SE) block. Noteworthily, to address the class imbalance problem, we design sampling strategies with probability compensation and propose a class-aware Sparse Weighted Dice and Focal (SWDF) loss function to improve the recognition rate for minority classes (N1) and hard-to-classify samples (N3) especially for OSA patients. Additionally, two hold-out sets containing healthy and sleep-disordered subjects are considered to verify the generalization. Facing the background of large-scale imbalanced heterogeneous data, we perform subject-wise 5-fold cross-validation on each dataset, and the results demonstrate that our model outperforms many methods, especially in N1, achieving an average overall accuracy, macro F1-score (MF1), and kappa of 84.62%, 79.6%, and 0.764 on heterogeneous datasets under optimal partitioning, providing a solid foundation for out-of-hospital sleep monitoring. Moreover, the overall standard deviation of MF1 under different folds remains within 0.175, indicating that the model is relatively stable.

Microbial Interaction is Among the Key Factors for Isolation of Previous Uncultured Microbes

Pure cultivation of microbes is still limited by the challenges of microbial uncultivability, with most microbial strains unable to be cultivated under standard laboratory conditions. The experience accumulated from advanced techniques such as in situ cultivation has identified that microbial interactions exist in natural habitats but are absent in laboratory cultures. These microbial interactions are likely one of the key factors in isolating previously uncultured microbes. The need for better knowledge of the mechanisms operating in microbial interactions has led to various experiments that have utilized microbial interactions in different approaches to microbial cultivation. These new attempts to understand microbial interactions not only present a new perspective on microbial uncultivability but also provide an opportunity to access uncultured phylogenetically novel microbes with their potential biotechnology applications. In this review, we focus on studies of the mechanisms of microbial interaction where the growth of other microbes is affected. Additionally, we review some successful applications of microbial interactions in cultivation methods, an approach that can play an important role in the bioprospecting of untapped microbial resources.

Categorical working memory codes in human visual cortex

Working memory contents are represented in neural activity patterns across multiple regions of the cortical hierarchy. A division of labor has been proposed where more anterior regions harbor increasingly abstract and categorical representations while the most detailed representations are held in primary sensory cortices. Here, using fMRI and multivariate encoding modeling, we demonstrate that for color stimuli categorical codes are already present at the level of extrastriate visual cortex (V4 and VO1), even when subjects are neither implicitly nor explicitly encouraged to categorize the stimuli. Importantly, this categorical coding was observed during working memory, but not during perception. Thus, visual working memory is likely to rely at least in part on categorical representations. SIGNIFICANCE STATEMENT: Working memory is the representational basis for human cognition. Recent work has demonstrated that numerous regions across the human brain can represent the contents of working memory. We use fMRI brain scanning and machine learning methods to demonstrate that different regions can represent the same content differently during working memory. Reading out the neural codes used to store working memory contents, we show that already in sensory cortex, areas V4 and VO1 represent color in a categorical format rather than a purely sensory fashion. Thereby, we provide a better understanding of how different regions of the brain might serve working memory and cognition.

CXCR2 expression during melanoma tumorigenesis controls transcriptional programs that facilitate tumor growth

BACKGROUND

Though the CXCR2 chemokine receptor is known to play a key role in cancer growth and response to therapy, a direct link between expression of CXCR2 in tumor progenitor cells during induction of tumorigenesis has not been established.

METHODS

To characterize the role of CXCR2 during melanoma tumorigenesis, we generated tamoxifen-inducible tyrosinase-promoter driven BrafV600E/Pten-/-/Cxcr2-/- and NRasQ61R/INK4a-/-/Cxcr2-/- melanoma models. In addition, the effects of a CXCR1/CXCR2 antagonist, SX-682, on melanoma tumorigenesis were evaluated in BrafV600E/Pten-/- and NRasQ61R/INK4a-/- mice and in melanoma cell lines. Potential mechanisms by which Cxcr2 affects melanoma tumorigenesis in these murine models were explored using RNAseq, mMCP-counter, ChIPseq, and qRT-PCR; flow cytometry, and reverse phosphoprotein analysis (RPPA).

RESULTS

Genetic loss of Cxcr2 or pharmacological inhibition of CXCR1/CXCR2 during melanoma tumor induction resulted in key changes in gene expression that reduced tumor incidence/growth and increased anti-tumor immunity. Interestingly, after Cxcr2 ablation, Tfcp2l1, a key tumor suppressive transcription factor, was the only gene significantly induced with a log2 fold-change greater than 2 in these three different melanoma models.

CONCLUSIONS

Here, we provide novel mechanistic insight revealing how loss of Cxcr2 expression/activity in melanoma tumor progenitor cells results in reduced tumor burden and creation of an anti-tumor immune microenvironment. This mechanism entails an increase in expression of the tumor suppressive transcription factor, Tfcp2l1, along with alteration in the expression of genes involved in growth regulation, tumor suppression, stemness, differentiation, and immune modulation. These gene expression changes are coincident with reduction in the activation of key growth regulatory pathways, including AKT and mTOR.

SSNMDI: a novel joint learning model of semi-supervised non-negative matrix factorization and data imputation for clustering of single-cell RNA-seq data

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) technology attracts extensive attention in the biomedical field. It can be used to measure gene expression and analyze the transcriptome at the single-cell level, enabling the identification of cell types based on unsupervised clustering. Data imputation and dimension reduction are conducted before clustering because scRNA-seq has a high 'dropout' rate, noise and linear inseparability. However, independence of dimension reduction, imputation and clustering cannot fully characterize the pattern of the scRNA-seq data, resulting in poor clustering performance. Herein, we propose a novel and accurate algorithm, SSNMDI, that utilizes a joint learning approach to simultaneously perform imputation, dimensionality reduction and cell clustering in a non-negative matrix factorization (NMF) framework. In addition, we integrate the cell annotation as prior information, then transform the joint learning into a semi-supervised NMF model. Through experiments on 14 datasets, we demonstrate that SSNMDI has a faster convergence speed, better dimensionality reduction performance and a more accurate cell clustering performance than previous methods, providing an accurate and robust strategy for analyzing scRNA-seq data. Biological analysis are also conducted to validate the biological significance of our method, including pseudotime analysis, gene ontology and survival analysis. We believe that we are among the first to introduce imputation, partial label information, dimension reduction and clustering to the single-cell field.

AVAILABILITY AND IMPLEMENTATION

The source code for SSNMDI is available at https://github.com/yushanqiu/SSNMDI.

Clustering-Fusion Feature Selection Method in Identifying Major Depressive Disorder Based on Resting State EEG Signals

Depression is a heterogeneous syndrome with certain individual differences among subjects. Exploring a feature selection method that can effectively mine the commonness intra-groups and the differences inter-groups in depression recognition is therefore of great significance. This study proposed a new clustering-fusion feature selection method. Hierarchical clustering (HC) algorithm was used to capture the heterogeneity distribution of subjects. Average and similarity network fusion (SNF) algorithms were adopted to characterize the brain network atlas of different populations. Differences analysis was also utilized to obtain the features with discriminant performance. Experiments showed that compared with traditional feature selection methods, HCSNF method yielded the optimal classification results of depression recognition in both sensor and source layers of electroencephalography (EEG) data. Especially in the beta band of EEG data at sensor layer, the classification performance was improved by more than 6%. Moreover, the long-distance connections between parietal-occipital lobe and other brain regions not only have high discriminative power, but also significantly correlate with depressive symptoms, indicating the important role of these features in depression recognition. Therefore, this study may provide methodological guidance for the discovery of reproducible electrophysiological biomarkers and new insights into common neuropathological mechanisms of heterogeneous depression diseases.

Towards texture accurate slice interpolation of medical images using PixelMiner

Quantitative image analysis models are used for medical imaging tasks such as registration, classification, object detection, and segmentation. For these models to be capable of making accurate predictions, they need valid and precise information. We propose PixelMiner, a convolution-based deep-learning model for interpolating computed tomography (CT) imaging slices. PixelMiner was designed to produce texture-accurate slice interpolations by trading off pixel accuracy for texture accuracy. PixelMiner was trained on a dataset of 7829 CT scans and validated using an external dataset. We demonstrated the model's effectiveness by using the structural similarity index (SSIM), peak signal to noise ratio (PSNR), and the root mean squared error (RMSE) of extracted texture features. Additionally, we developed and used a new metric, the mean squared mapped feature error (MSMFE). The performance of PixelMiner was compared to four other interpolation methods: (tri-)linear, (tri-)cubic, windowed sinc (WS), and nearest neighbor (NN). PixelMiner produced texture with a significantly lowest average texture error compared to all other methods with a normalized root mean squared error (NRMSE) of 0.11 (p < .01), and the significantly highest reproducibility with a concordance correlation coefficient (CCC) ≥ 0.85 (p < .01). PixelMiner was not only shown to better preserve features but was also validated using an ablation study by removing auto-regression from the model and was shown to improve segmentations on interpolated slices.

Label Propagation via Random Walk for Training Robust Thalamus Nuclei Parcellation Model from Noisy Annotations

Data-driven thalamic nuclei parcellation depends on high-quality manual annotations. However, the small size and low contrast changes among thalamic nuclei, yield annotations that are often incomplete, noisy, or ambiguously labelled. To train a robust thalamic nuclei parcellation model with noisy annotations, we propose a label propagation algorithm based on random walker to refine the annotations before model training. A two-step model was trained to generate first the whole thalamus and then the nuclei masks. We conducted experiments on a mild traumatic brain injury~(mTBI) dataset with noisy thalamic nuclei annotations. Our model outperforms current state-of-the-art thalamic nuclei parcellations by a clear margin. We believe our method can also facilitate the training of other parcellation models with noisy labels.

[Prevalence of colorectal cancer in 2020: a comparative analysis between China and the world]

Objective: To demonstrate the disease burden and epidemiological characteristics of colorectal cancer in different regions by analyzing the incidence and mortality data in China and worldwide in 2020. Methods: Estimation of the incidence and mortality data of colorectal cancer were obtained from the GLOBOCAN 2020 database. The incidence, death, age standardized incidence rate (ASIR) and age standardized mortality rate (ASMR) of colorectal cancer in China and 20 regions in the world were compared. The correlation between the Human Development Index (HDI) and ASIR/ASMR was analyzed. Results: In 2020, the number of new cases of colorectal cancer in the world reached 1 931 600, and the number of deaths reached 935 200. The incidence and mortality in all regions of the world continued to rise in the age group above 50 years old. The morbidity and mortality in male were higher than those in female. East Asia ranked the highest number of incidence cases and deaths in the world, which were 740 000 and 360 100 respectively. There were significant differences in incidence and mortality among regions in the world. The highest ASIR and ASMR were observed in Northern Europe (33.61/100 000) and Eastern Europe (14.53/100 000), whereas the lowest ASIR and ASMR were both observed in South-Central Asia (5.46/100 000 and 3.16/100 000). HDI had significant exponential relationship with ASIR (r(2)=0.59, P<0.001) and ASMR (r(2)=0.38, P<0.001). There were 555 500 new cases and 286 200 death cases of colorectal cancer in China, accounting for about 30% of the world and more than 75% of East Asia. The ASIR of China was 24.07/100 000, ranking at the medium level, while the ASMR was 12.07/100 000, ranking at the high level of world. Conclusion: The incidence and mortality of colorectal cancer are highly correlated with HDI. China is one of the countries with the heaviest disease burden of colorectal cancer in the world.

Mid-infrared silicon metasurfaces for near-field enhancement of molecular fingerprints

Mid-infrared dielectric metasurfaces are promising fundamental building blocks for integrated sensing with high sensitivity, compositional selectivity, and low loss. We have designed and fabricated a silicon metasurface with resonance properties in the 4∼5 µm mid-infrared region and a volume enhancement of up to 9 times. Benchmark FTIR characterizations of solutions of tungsten hexacarbonyl molecules showed a detection limit of 1 mg/mL without the usage of surface enrichment treatment. We further rationalize the detection limit of the molecules-nanostructure open interface with volume field enhancement analysis. Our results show that mid-infrared silicon metasurfaces may be a suitable platform for potential integration with microfluidic for in vivo detection.

Polylactic-co-glycolic acid-based nanoparticles modified with peptides and other linkers cross the blood-brain barrier for targeted drug delivery

Because of the blood-brain barrier, only a limited fraction of drugs can penetrate the brain. As a result, there is a need to take larger doses of the drug, which may result in numerous undesirable side effects. Over the past few decades, a plethora of research has been conducted to address this issue. In recent years, the field of nanomedicine research has reported promising findings. Currently, numerous types of polylactic-co-glycolic acid-based drug-delivery systems are being studied, and great progress has been made in the modification of their surfaces with a variety of ligands. In this review, the authors highlight the preparation of polylactic-co-glycolic acid-based nanoparticles and single- and dual-targeted peptide modifications for site-specific drug delivery into the brain.

CXCR2 expression during melanoma tumorigenesis controls transcriptional programs that facilitate tumor growth

Background

Though the CXCR2 chemokine receptor is known to play a key role in cancer growth and response to therapy, a direct link between expression of CXCR2 in tumor progenitor cells during induction of tumorigenesis has not been established.

Methods

To characterize the role of CXCR2 during melanoma tumorigenesis, we generated tamoxifen-inducible tyrosinase-promoter driven Braf ^V600E /Pten ^-/- /Cxcr2 ^-/- and NRas ^Q61R /INK4a ^-/- /Cxcr2 ^-/- melanoma models. In addition, the effects of a CXCR1/CXCR2 antagonist, SX-682, on melanoma tumorigenesis were evaluated in Braf ^V600E /Pten ^-/- and NRas ^Q61R /INK4a ^-/- mice and in melanoma cell lines. Potential mechanisms by which Cxcr2 affects melanoma tumorigenesis in these murine models were explored using RNAseq, mMCP-counter, ChIPseq, and qRT-PCR; flow cytometry, and reverse phosphoprotein analysis (RPPA).

Results

Genetic loss of Cxcr2 or pharmacological inhibition of CXCR1/CXCR2 during melanoma tumor induction resulted in key changes in gene expression that reduced tumor incidence/growth and increased anti-tumor immunity. Interestingly, after Cxcr2 ablation, Tfcp2l1 , a key tumor suppressive transcription factor, was the only gene significantly induced with a log ₂ fold-change greater than 2 in these three different melanoma models.

Conclusions

Here, we provide novel mechanistic insight revealing how loss of Cxcr2 expression/activity in melanoma tumor progenitor cells results in reduced tumor burden and creation of an anti-tumor immune microenvironment. This mechanism entails an increase in expression of the tumor suppressive transcription factor, Tfcp2l1, along with alteration in the expression of genes involved in growth regulation, tumor suppression, stemness, differentiation, and immune modulation. These gene expression changes are coincident with reduction in the activation of key growth regulatory pathways, including AKT and mTOR.

scHOIS: Determining Cell Heterogeneity Through Hierarchical Clustering Based on Optimal Imputation Strategy

Advances in single-cell RNA sequencing (scRNA-seq) technology provide an unbiased and high-throughput analysis of each cell at single-cell resolution, and further facilitate the development of cellular heterogeneity analysis. Despite the promise of scRNA-seq, the data generated by this method are sparse and noisy because of the presence of dropout events, which can greatly impact downstream analyses such as differential gene expression, cell type annotation, and linage trajectory reconstruction. The development of effective and robust computational methods to address both dropout and clustering are thus urgently needed. In this study, we propose a flexible, accurate two-stage algorithm for single cell heterogeneity analysis via hierarchical clustering based on an optimal imputation strategy, called scHOIS. At the first stage, masked non-negative matrix factorization is applied to approximate the original observed scRNA-seq data, with optimal rank determined by variance analysis. At the second stage, hierarchical clustering is applied to group the imputed cells using Pearson correlation to measure similarity, with the optimal number of clusters determined by integrating three classical indexes. We performed extensive experiments on real-world datasets, which showed that scHOIS effectively and robustly distinguished cellular differences and that the clustering performance of this algorithm was superior to that of other state-of-the-art methods.

Tailoring particle size of PbI2 towards efficient perovskite solar cells under ambient air conditions

The PbI2 films as templates are essential to the quality of perovskite (PVK) film in two-step sequential deposition process. Herein, we regulate PbI2 films by tailoring the particle size of...

Sleep Quality Evaluation Based on Single-Lead Wearable Cardiac Cycle Acquisition Device

In clinical conditions, polysomnography (PSG) is regarded as the "golden standard" for detecting sleep disease and offering a reference of objective sleep quality. For healthy adults, scores from sleep questionnaires are more reliable than other methods in obtaining knowledge of subjective sleep quality. In practice, the need to simplify PSG to obtain subjective sleep quality by recording a few channels of physiological signals such as single-lead electrocardiogram (ECG) or photoplethysmography (PPG) signal is still very urgent. This study provided a two-step method to differentiate sleep quality into "good sleep" and "poor sleep" based on the single-lead wearable cardiac cycle data, with the comparison of the subjective sleep questionnaire score. First, heart rate variability (HRV) features and ECG-derived respiration features were extracted to construct a sleep staging model (wakefulness (W), rapid eye movement (REM), light sleep (N1&N2) and deep sleep (N3)) using the multi-classifier fusion method. Then, features extracted from the sleep staging results were used to construct a sleep quality evaluation model, i.e., classifying the sleep quality as good and poor. The accuracy of the sleep staging model, tested on the international public database, was 0.661 and 0.659 in Cardiology Challenge 2018 training database and Sleep Heart Health Study Visit 1 database, respectively. The accuracy of the sleep quality evaluation model was 0.786 for our recording subjects, with an average F₁-score of 0.771. The proposed sleep staging model and sleep quality evaluation model only requires one channel of wearable cardiac cycle signal. It is very easy to transplant to portable devices, which facilitates daily sleep health monitoring.

The Mechanical Behavior and Enhancement Mechanism of Short Carbon Fiber Reinforced AFS Interface

The aluminum foam sandwich (AFS), which perfectly combines the excellent merits of an aluminum foam core and face sheet materials, has extensive and reliable applications in many fields, such as aerospace, military equipment, transportation, and so on. Adhesive bonding is one of the most widely used methods to produce AFS due to its general applicability, simple process, and low cost, however, the bonding interface is known as the weak link and may cause a serious accident. To overcome the shortcomings of a bonded AFS interface, short carbon fiber as a reinforcement phase was introduced to epoxy resin to reinforce the interface adhesion strength of AFS. Single lap shear tests and three-point bending tests were conducted to study the mechanical behavior of the reinforced interface and AFS, respectively. The failure mechanism was studied through a macro- and microanalysis. The result showed that after the reinforcement of carbon fiber, the tangential shear strength of the interface increased by 73.65%. The effective displacement of AFS prepared by the reinforced epoxy resin is 125.95% more than the AFS prepared by the unreinforced epoxy resin. The flexure behavior of the reinforced AFS can be compared with AFS made through a metallurgical method. Three categories of reinforcement mechanisms were discovered: (a) the pull off and pull mechanism: when the modified carbon fiber performed as the bridge, the bonding strength improved because of the pull off and pull out of fibers; (b) adhesion effect: the carbon fiber gathered in the hole edge resulted in epoxy resins being gathered in there too, which increased the effective bonding area of the interface; (c) mechanical self-locking effect: the carbon fiber enhanced the adhesive filling performance of aluminum foam holes, which improved the mechanical self-locking effect of the bonding interface.

10.3% Efficient Green Cd-Free Cu2 ZnSnS4 Solar Cells Enabled by Liquid-Phase Promoted Grain Growth

Small grain size and near-horizontal grain boundaries are known to be detrimental to the carrier collection efficiency and device performance of pure-sulfide Cu₂ ZnSnS₄ (CZTS) solar cells. However, forming large grains spanning the absorber layer while maintaining high electronic quality is challenging particularly for pure sulfide CZTS. Herein, a liquid-phase-assisted grain growth (LGG) model that enables the formation of large grains spanning across the CZTS absorber without compromising the electronic quality is demonstrated. By introducing a Ge-alloyed CZTS nanoparticle layer at the bottom of the sputtered precursor, a Cu-rich and Sn-rich liquid phase forms at the high temperature sulfurization stage, which can effectively remove the detrimental near-horizontal grain boundaries and promote grain growth, thus greatly improving the carrier collection efficiency and reducing nonradiative recombination. The remaining liquid phase layer at the rear interface shows a high work function, acting as an effective hole transport layer. The modified morphology greatly increases the short-circuit current density and fill factor, enabling 10.3% efficient green Cd-free CZTS devices. This work unlocks a grain growth mechanism, advancing the morphology control of sulfide-based kesterite solar cells.

A Novel ECG-Derived Respiration Method Combining Frequency-Domain Feature and Interacting Multiple Model Smoother

OBJECTIVE

ECG-derived respiration (EDR) is a low-cost and productive means for capturing respiratory activity. In particular, as the primary procedure in some cardiorespiratory-related studies, the quality of EDR is decisive for the performance of subsequent analyses.

APPROACH

In this paper, we proposed a novel EDR method based on the feature derived from the first moment (mean frequency) of the power spectrum (FMS). After obtaining the EDR signal from the feature, we introduced the Interacting Multiple Model (IMM) smoother to enhance the similarity of the EDR signal to the reference respiration. The assessment of the approach consisted of two steps: (1) the performance of extracted feature was verified against R-peak misalignment and noise. (2) the enhancement of IMM smoother to EDR waveforms was evaluated based on waveform correlation and respiratory rate estimation. All the assessments were conducted under the Fantasia database and Drivers database.

RESULTS

The FMS improved robustness against R peak offsets compared to most established feature-based EDR algorithms, but a slight 5% improvement of waveform correlation against RR interval-based feature under accurate R peaks. The IMM smoother performed similarly with the Kalman filter in the static database but realized the enhancement of some extent of the EDR waveform in the ambulatory database.

SIGNIFICANCE

The proposed method investigated frequency domain mapping of ECG morphological changes caused by respiratory modulation and explained the EDR signal as a non-stationary time series, which provided a direction of better fitting the natural respiration process and enhancing the EDR waveform.

A pilot study of deep learning-based CT volumetry for traumatic hemothorax

PURPOSE

We employ nnU-Net, a state-of-the-art self-configuring deep learning-based semantic segmentation method for quantitative visualization of hemothorax (HTX) in trauma patients, and assess performance using a combination of overlap and volume-based metrics. The accuracy of hemothorax volumes for predicting a composite of hemorrhage-related outcomes - massive transfusion (MT) and in-hospital mortality (IHM) not related to traumatic brain injury - is assessed and compared to subjective expert consensus grading by an experienced chest and emergency radiologist.

MATERIALS AND METHODS

The study included manually labeled admission chest CTs from 77 consecutive adult patients with non-negligible (≥ 50 mL) traumatic HTX between 2016 and 2018 from one trauma center. DL results of ensembled nnU-Net were determined from fivefold cross-validation and compared to individual 2D, 3D, and cascaded 3D nnU-Net results using the Dice similarity coefficient (DSC) and volume similarity index. Pearson's r, intraclass correlation coefficient (ICC), and mean bias were also determined for the best performing model. Manual and automated hemothorax volumes and subjective hemothorax volume grades were analyzed as predictors of MT and IHM using AUC comparison. Volume cut-offs yielding sensitivity or specificity ≥ 90% were determined from ROC analysis.

RESULTS

Ensembled nnU-Net achieved a mean DSC of 0.75 (SD: ± 0.12), and mean volume similarity of 0.91 (SD: ± 0.10), Pearson r of 0.93, and ICC of 0.92. Mean overmeasurement bias was only 1.7 mL despite a range of manual HTX volumes from 35 to 1503 mL (median: 178 mL). AUC of automated volumes for the composite outcome was 0.74 (95%CI: 0.58-0.91), compared to 0.76 (95%CI: 0.58-0.93) for manual volumes, and 0.76 (95%CI: 0.62-0.90) for consensus expert grading (p = 0.93). Automated volume cut-offs of 77 mL and 334 mL predicted the outcome with 93% sensitivity and 90% specificity respectively.

CONCLUSION

Automated HTX volumetry had high method validity, yielded interpretable visual results, and had similar performance for the hemorrhage-related outcomes assessed compared to manual volumes and expert consensus grading. The results suggest promising avenues for automated HTX volumetry in research and clinical care.

Facet-selective etching trajectories of individual semiconductor nanocrystals

The size and shape of semiconductor nanocrystals govern their optical and electronic properties. Liquid cell transmission electron microscopy (LCTEM) is an emerging tool that can directly visualize nanoscale chemical transformations and therefore inform the precise synthesis of nanostructures with desired functions. However, it remains difficult to controllably investigate the reactions of semiconductor nanocrystals with LCTEM, because of the highly reactive environment formed by radiolysis of liquid. Here, we harness the radiolysis processes and report the single-particle etching trajectories of prototypical semiconductor nanomaterials with well-defined crystalline facets. Lead selenide nanocubes represent an isotropic structure that retains the cubic shape during etching via a layer-by-layer mechanism. The anisotropic arrow-shaped cadmium selenide nanorods have polar facets terminated by either cadmium or selenium atoms, and the transformation trajectory is driven by etching the selenium-terminated facets. LCTEM trajectories reveal how nanoscale shape transformations of semiconductors are governed by the reactivity of specific facets in liquid environments.

Lactate promotes metastasis of normoxic colorectal cancer stem cells through PGC-1α-mediated oxidative phosphorylation

Uneven oxygen supply in solid tumors leads to hypoxic and normoxic regions. Hypoxic cells exhibit increased secretion of lactate, which creates an acidic tumor microenvironment (TME). This acidic TME is positively associated with tumor metastasis. Despite the increased metastatic capacity of hypoxic cells, they are located relatively further away from the blood vessels and have limited access to the circulatory system. Studies have shown that cancer stem cells (CSCs) are enriched for tumor metastasis-initiating cells and generally undergo aerobic respiration, which could be enhanced by lactate. We therefore hypothesized that TME-derived lactate may promote the metastasis of normoxic CSCs. In the present study, the abundance of hypoxic and normoxic CSCs was analyzed in primary CRC tumors. It was found that the proportion of normoxic CSCs was positively associated with tumor stage. Using two human CRC cell lines, LoVo and SW480, and a patient-derived xenograft (XhCRC), it was found that treatment with lactate promoted normoxic CSC metastasis. Metabolism analysis indicated that, upon treatment with lactate, oxidative phosphorylation (OXPHOS) activity in normoxic CSCs was enhanced, whereas hypoxic CSCs were rarely altered. At the molecular level, the expression of peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α), a master regulator of lactate oxidation, was found to be elevated in normoxic CSCs. Furthermore, PGC-1α knockdown markedly reduced the metastatic potential of normoxic CSCs. Notably, both the PGC-1α-mediated OXPHOS activity and metastatic potential were impaired when hypoxia-inducible factor-1α (HIF-1α) was activated in normoxic CSCs. Together, these findings provide a therapeutic strategy against tumor metastasis through the targeting of PGC-1α and, thus, the suppression of lactate-feeding OXPHOS in normoxic CSCs may improve the therapeutic benefit of patients with cancer, particularly CRC.

Abnormal Brain Topological Structure of Mild Depression During Visual Search Processing Based on EEG Signals

Studies have shown that attention bias can affect behavioral indicators in patients with depression, but it is still unclear how this bias affects the brain network topology of patients with mild depression (MD). Therefore, a novel functional brain network analysis and hierarchical clustering methods were used to explore the abnormal brain topology of MD patients based on EEG signals during the visual search paradigm. The behavior results showed that the reaction time of MD group was significantly higher than that of normal group. The results of functional brain network indicated significant differences in functional connections between the two groups, the amount of inter-hemispheric long-distance connections are much larger than intra-hemispheric short-distance connections. Patients with MD showed significantly lower local efficiency and clustering coefficient, destroyed community structure of frontal lobe and parietal-occipital lobe, frontal asymmetry, especially in beta band. In addition, the average value of long-distance connections between left frontal and right parietal-occipital lobes presented significant correlation with depressive symptoms. Our results suggested that MD patients achieved long-distance connections between the frontal and parietal-occipital regions by sacrificing the connections within the regions, which might provide new insights into the abnormal cognitive processing mechanism of depression.

Biogenic volatile organic compound emission patterns and secondary pollutant formation potentials of dominant greening trees in Chengdu, southwest China

Integral to the urban ecosystem, greening trees provide many ecological benefits, but the active biogenic volatile organic compounds (BVOCs) they release contribute to the production of ozone and secondary organic aerosols, which harm ambient air quality. It is, therefore, necessary to understand the BVOC emission characteristics of dominant greening tree species and their relative contribution to secondary pollutants in various urban contexts. Consequently, this study utilized a dynamic enclosure system to collect BVOC samples of seven dominant greening tree species in urban Chengdu, Southwest China. Gas chromatography/mass spectrometry was used to analyze the BVOC components and standardized BVOC emission rates of each tree species were then calculated to assess their relative potential to form secondary pollutants. We found obvious differences in the composition of BVOCs emitted by each species. Ficus virens displayed a high isoprene emission rate at 31.472 μgC/(gdw (g dry weight)•hr), while Cinnamomum camphora emitted high volumes of D-Limonene at 93.574 μgC/(gdw•hr). In terms of the BVOC emission rates by leaf area, C. camphora had the highest emission rate of total BVOCs at 13,782.59 μgC/(m²•hr), followed by Cedrus deodara with 5466.86 μgC/(m²•hr). Ginkgo biloba and Osmanthus fragrans mainly emitted oxygenated VOCs with lower overall emission rates. The high BVOC emitters like F. virens, C. camphora, and Magnolia grandiflora have high potential for significantly contributing to environmental secondary pollutants, so should be cautiously considered for future planting. This study provides important implications for improving urban greening efforts for subtropical Chinese urban contexts, like Chengdu.

Observed coupling between air mass history, secondary growth of nucleation mode particles and aerosol pollution levels in Beijing

Atmospheric aerosols have significant effects on the climate and on human health. New particle formation (NPF) is globally an important source of aerosols but its relevance especially towards aerosol mass loadings in highly polluted regions is still controversial. In addition, uncertainties remain regarding the processes leading to severe pollution episodes, concerning e.g. the role of atmospheric transport. In this study, we utilize air mass history analysis in combination with different fields related to the intensity of anthropogenic emissions in order to calculate air mass exposure to anthropogenic emissions (AME) prior to their arrival at Beijing, China. The AME is used as a semi-quantitative metric for describing the effect of air mass history on the potential for aerosol formation. We show that NPF events occur in clean air masses, described by low AME. However, increasing AME seems to be required for substantial growth of nucleation mode (diameter < 30 nm) particles, originating either from NPF or direct emissions, into larger mass-relevant sizes. This finding assists in establishing and understanding the connection between small nucleation mode particles, secondary aerosol formation and the development of pollution episodes. We further use the AME, in combination with basic meteorological variables, for developing a simple and easy-to-apply regression model to predict aerosol volume and mass concentrations. Since the model directly only accounts for changes in meteorological conditions, it can also be used to estimate the influence of emission changes on pollution levels. We apply the developed model to briefly investigate the effects of the COVID-19 lockdown on PM_2.5 concentrations in Beijing. While no clear influence directly attributable to the lockdown measures is found, the results are in line with other studies utilizing more widely applied approaches.

Entropy Analysis of Heart Rate Variability in Different Sleep Stages

How the complexity or irregularity of heart rate variability (HRV) changes across different sleep stages and the importance of these features in sleep staging are not fully understood. This study aimed to investigate the complexity or irregularity of the RR interval time series in different sleep stages and explore their values in sleep staging. We performed approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), distribution entropy (DistEn), conditional entropy (CE), and permutation entropy (PermEn) analyses on RR interval time series extracted from epochs that were constructed based on two methods: (1) 270-s epoch length and (2) 300-s epoch length. To test whether adding the entropy measures can improve the accuracy of sleep staging using linear HRV indices, XGBoost was used to examine the abilities to differentiate among: (i) 5 classes [Wake (W), non-rapid-eye-movement (NREM), which can be divide into 3 sub-stages: stage N1, stage N2, and stage N3, and rapid-eye-movement (REM)]; (ii) 4 classes [W, light sleep (combined N1 and N2), deep sleep (N3), and REM]; and (iii) 3 classes: (W, NREM, and REM). SampEn, FuzzyEn, and CE significantly increased from W to N3 and decreased in REM. DistEn increased from W to N1, decreased in N2, and further decreased in N3; it increased in REM. The average accuracy of the three tasks using linear and entropy features were 42.1%, 59.1%, and 60.8%, respectively, based on 270-s epoch length; all were significantly lower than the performance based on 300-s epoch length (i.e., 54.3%, 63.1%, and 67.5%, respectively). Adding entropy measures to the XGBoost model of linear parameters did not significantly improve the classification performance. However, entropy measures, especially PermEn, DistEn, and FuzzyEn, demonstrated greater importance than most of the linear parameters in the XGBoost model.300-s270-s.

Large-Grain Spanning Monolayer Cu2 ZnSnSe4 Thin-Film Solar Cells Grown from Metal Precursor

The persistent double layer structure whereby two layers with different properties form at the front and rear of absorbers is a critical challenge in the field of kesterite thin-film solar cells, which imposes additional nonradiative recombination in the quasi-neutral region and potential limitation to the transport of hole carriers. Herein, an effective model for growing monolayer CZTSe thin-films based on metal precursors with large grains spanning the whole film is developed. Voids and fine grain layer are avoided successfully by suppressing the formation of a Sn-rich liquid metal phase near Mo back contact during alloying, while grain coarsening is greatly promoted by enhancing mass transfer during grain growth. The desired morphology exhibits several encouraging features, including significantly reduced recombination in the quasi-neutral region that contributes to the large increase of short-circuit current, and a quasi-Ohmic back contact which is a prerequisite for high fill factor. Though this growth mode may introduce more interfacial defects which require further modification, the strategies demonstrated remove a primary obstacle toward higher efficiency kesterite solar cells, and can be applicable to morphology control with other emerging chalcogenide thin films.