In situ investigation of ice fractions and water states during partial freezing of pork loins and shrimps.

Ice fractions and water states in partially frozen muscle foods greatly affect their quality. In the study, a variable temperature nuclear magnetic resonance (VT-NMR) with a liquid nitrogen temperature control system was employed to in situ investigate the relationship between ice fractions and temperatures and changes in water states during partial freezing and thawing of pork and shrimp. Results indicated that changes in ice fractions ranging from -2 âˆ¼ -20 °C could be divided into 3 stages including slow increase, random leap and remarkable leap. More serious damages to the structures related to immobile water occurred in shrimp than in pork, and partial freezing also caused deterioration in muscle fibres related to free water. Additionally, -2 âˆ¼ -3 °C and - 3.5 °C were the appropriate partial freezing temperatures for pork and shrimp, respectively. Therefore, the VT-NMR method possessed great potential for fundamental studies and applications of partial freezing of muscle foods.

Tunable thermoresponsive hydrogels for temperature regulation and warning in fruit and vegetables preservation.

Fresh fruit and vegetables usually suffer from quality deterioration when exposed to inappropriate temperatures. Common energy-input temperature regulation is widely applied but there remain challenges of increasing energy consumption. Passive temperature management regulates the heat transfer without energy consumption, showing a sustainable strategy for food preservation. Here, thermoresponsive hydrogels were constructed by incorporating NaCl and sodium dodecyl sulfate (SDS) micelles into a poly(N-isopropylacrylamide-co-acrylamide) (P(NIPAM-co-AM)) network. Due to the excellent mechanical properties and reversible thermochromism at 14 °C and 37 °C, Gel-8 wt%-NaCl could inhibit temperature rise and avoid sunburn damage to peppers under direct sunlight by blocking the input of solar energy and accelerating moisture evaporation. Additionally, hydrogels could act as a feasible sensor by providing real-time visual warnings for inappropriate temperatures during banana storage. Based on the self-adaptive thermoresponsive behaviour, the prepared hydrogels showed effective performance of temperature regulation and quality preservation of fruit and vegetables.

Unveiling Novel Structural Biomarkers for the Diagnosis of Glaucoma.

Glaucoma, a leading cause of irreversible blindness, poses a significant global health burden. Early detection is crucial for effective management and prevention of vision loss. This study presents a collection of novel structural biomarkers in glaucoma diagnosis. By employing advanced imaging techniques and data analysis algorithms, we now can recognize indicators of glaucomatous progression. Many research studies have revealed a correlation between the structural changes in the eye or brain, particularly in the optic nerve head and retinal nerve fiber layer, and the progression of glaucoma. These biomarkers demonstrate value in distinguishing glaucomatous eyes from healthy ones, even in the early stages of the disease. By facilitating timely detection and monitoring, they hold the potential to mitigate vision impairment and improve patient outcomes. This study marks an advancement in the field of glaucoma, offering a promising avenue for enhancing the diagnosis and possible management.

Association between central serous chorioretinopathy and Helicobacter pylori infection: a systematic review and Meta-analysis.

AIM: To investigate the association between central serous chorioretinopathy (CSC) and Helicobacter pylori (Hp) by summarizing all available evidence. METHODS: The Scopus, Embase, EBSCO, PubMed, Web of Science, and Cochrane Library databases for all relevant studies published from inception to October 2022 were searched, and manually searched for relevant reference lists as a supplement. Studies investigating the association between CSC and Hp infection were included. Finally, 8 case-control studies were included in the Meta-analysis after study selection. RESULTS: The results showed no significant correlation between Hp infection and CSC [odds ratio (OR) 1.89, 95% confidential interval (CI) 0.58-6.15, I 2=96%, P=0.29]. After subgroup analysis based on the degree of development of the study (developing/developed countries), it was found that the results of the two subgroups were the same as the whole, and no significant difference between the two subgroups existed. Meta-regression showed that the effect of sample size on heterogeneity among studies was more prominent (P<0.01, adjusted R 2=89.72%), which can explain 89.72% of the sources of heterogeneity. CONCLUSION: This Meta-analysis reveals no significant correlation between Hp infection and CSC, which still warrants further well-designed extensive sample studies to reach a more reliable conclusion and promote a better understanding of the treatment of CSC.

The Application of Rho Kinase Inhibitors in the Management of Glaucoma.

Glaucoma is a chronic neurodegenerative disease that poses a significant threat of irreversible blindness worldwide. Current treatments for glaucoma focus on reducing intraocular pressure (IOP), which is the only modifiable risk factor. Traditional anti-glaucomatous agents, including carbonic anhydrase inhibitors, beta-blockers, alpha-2 agonists, and prostaglandin analogs, work by either improving uveoscleral outflow or reducing aqueous humor production. Rho kinase (ROCK) inhibitors represent a novel class of anti-glaucomatous drugs that have emerged from bench to bedside in the past decade, offering multifunctional characteristics. Unlike conventional medications, ROCK inhibitors directly target the trabecular meshwork outflow pathway. This review aims to discuss the mechanism of ROCK inhibitors in reducing IOP, providing neuroprotection, and preventing fibrosis. We also highlight recent studies and clinical trials evaluating the efficacy and safety of ROCK inhibitors, compare them with other clinical anti-glaucomatous medications, and outline future prospects for ROCK inhibitors in glaucoma treatment.

Prediction of freezing point and moisture distribution of beef with dual freeze-thaw cycles using hyperspectral imaging.

The freezing point (FP) is an important quality indicator of the superchilled meat. Currently, the potential of hyperspectral imaging (HSI) for predicting beef FP as affected by multiple freeze-thaw (F-T) cycles was explored. Correlation analysis revealed that the FP had a negative correlation with the proportion of bound water (P21) and a positive correlation with the proportion of immobilized water (P22). Moreover, the optimal wavelengths were selected by principal component analysis (PCA). Principal component regression (PCR) and partial least squares regression (PLSR) models were successfully developed based on the optimal wavelengths for predicting FP with determination coefficient in prediction (RP2) of 0.76, 0.76 and root mean square errors in prediction (RMSEP) of 0.12, 0.12, respectively. Additionally, PLSR based on full wavelengths was established for predicting P21 with RP2 of 0.80 and RMSEP of 0.67, and PLSR based on the optimal wavelengths was established for predicting P22 with RP2 of 0.87 and RMSEP of 0.66. The results show the potential of hyperspectral technology to predict the FP and moisture distribution of meat as a nondestructive method.

Mechanism of lncRNA SNHG16 on kidney clear cell carcinoma cells by targeting miR-506-3p/ETS1/RAS/ERK molecular axis.

Objective: This study aimed to investigate the mechanism of long noncoding ribonucleic acid (lncRNA) SNHG16 on kidney clear cell carcinoma (KIRC) cells by targeting miR-506-3p/ETS proto-oncogene 1, transcription factor (ETS1)/RAS/Extracellular regulated protein kinases (ERK) molecular axis, thus to provide reference for clinical diagnosis and treatment of KIRC in the future. Methods: Thirty-six patients with KIRC were enrolled in this study, and their carcinoma tissues and adjacent tissues were obtained for the detection of SNHG16/miR-506-3p/ETS1/RAS/ERK expression. Then, over-expressed SNHG16 plasmid and silenced plasmid were transfected into KIRC cells to observe the changes of their biological behavior. Results: SNHG16 and ETS1 were highly expressed while miR-506- 3p was low expressed in KIRC tissues; the RAS/ERK signaling pathway was significantly activated in KIRC tissues (P < 0.05). After SNHG16 silence, KIRC cells showed decreased proliferation, invasion and migration capabilities and increased apoptosis rate; correspondingly, increase in SNHG16 expression achieved opposite results (P < 0.05). Finally, in the rescue experiment, the effects of elevated SNHG16 on KIRC cells were reversed by simultaneous increase in miR-506-3p, and the effects of miR-506-3p were reversed by ETS1. Activation of the RAS/ERK pathway had the same effect as increase in ETS1, which further worsened the malignancy of KIRC. After miR-506-3p increase and ETS1 silence, the RAS/ERK signaling pathway was inhibited (P < 0.05). At last, the rescue experiment (co-transfection) confirmed that the effect of SNHG16 on KIRC cells is achieved via the miR-506-3p/ETS1/RAS/ERK molecular axis. Conclusion: SNHG16 regulates the biological behavior of KIRC cells by targeting the miR-506-3p/ETS1/RAS/ERK molecular axis.

In-situ investigation of supercooling behaviour during high-pressure shift freezing of pure water and sucrose solution.

Supercooling is a main controllable factor for the fundamental understanding the high-pressure shift freezing (HPSF). In the study, a self-developed device based on the diamond anvil cell (DAC) and confocal Raman microscopy was utilized to realize an in-situ investigation of supercooling behaviour during HPSF of the pure water and sucrose solution. The spectra were used to determine the freezing point which is shown as a spectral phase marker (SD). The hydrogen bond strengths of water and sucrose solution under supercooling states were estimated by peak position and peak area ratio of sub-peaks. The results showed that the OH stretching bands had redshift under supercooling states. Moreover, the addition of sucrose molecules could strengthen the hydrogen bonding strength of water molecules under supercooling states. Thus, the DAC combined with Raman spectroscopy could be considered a novel strategy for a deep understanding of the supercooling behaviour during HPSF.

A tailored dual core-shell magnetic SERS substrate with precise shell-thickness control for trace organophosphorus pesticides residues detection.

For addressing the challenges of strong affinity SERS substrate to organophosphorus pesticides (OPs), herein, a rapid water-assisted layer-by-layer heteronuclear growth method was investigated to grow uniform UiO-66 shell with controllable thickness outside the magnetic core and provide abundant defect sites for OPs adsorption. By further assembling the tailored Au@Ag, a highly sensitive SERS substrate Fe3O4-COOH@UiO-66/Au@Ag (FCUAA) was synthesized with a SERS enhancement factor of 2.11 × 107. The substrate's suitability for the actual vegetable samples (cowpeas and peppers) was confirmed under both destructive and non-destructive detection conditions, showing a strong SERS response to fenthion and triazophos, with limits of detection of 1.21 × 10-5 and 2.96 × 10-3 mg/kg in the vegetables under destructive conditions, and 0.13 and 1.39 ng/cm2 for non-destructive detection, respectively. The FCUAA substrate had high SERS performance, effective adsorption capability for OPs, and demonstrated good applicability, thus exhibiting great potential for rapid detection of trace OPs residues in the food industry.

Effects of cold plasma pretreatment combined with sodium periodate on property enhancement of dialdehyde starch prepared using native maize starch.

This study represents the inaugural investigation into the effect of cold plasma (CP) pretreatment combined with sodium periodate on the preparation of dialdehyde starch (DAS) from native maize starch and its consequent effects on the properties of DAS. The findings indicate that the maize starch underwent etching by the plasma, leading to an increase in the particle size of the starch, which in turn weakened the rigid structure of the starch and reduced its crystallinity. Concurrently, the plasma treatment induced cleavage of the starch molecular chain, resulting in a decrease in the viscosity of the starch and an enhancement of its fluidity. These alterations facilitated an increased contact area between the starch and the oxidising agent sodium periodate, thereby augmenting the efficiency of the DAS preparation reaction. Consequently, the aldehyde group content was elevated by 9.98 % compared to the conventional DAS preparation methodology. Therefore, CP could be an efficient and environmentally friendly non-thermal processing method to assist starch oxidation for DAS preparation and property enhancement.

Developing a magnetic SERS nanosensor utilizing aminated Fe-Based MOF for ultrasensitive trace detection of organophosphorus pesticides in apple juice.

The unreasonable use of organophosphorus pesticides leads to excessive pesticide residues in food, seriously threatening public health, and the potential of surface-enhanced Raman spectroscopy (SERS) technology, incorporating a metal-organic framework, is substantial for the rapid detection of trace pesticide residues. Here, a novel Fe3O4@NH2-MIL-101(Fe)@Ag (FNMA) SERS nanosensor was developed. Results indicated that the FNMA had a high enhancement factor of 1.53 × 108, a low limit of detection (LOD) of 4.55 × 10-12 M, and a relative standard deviation of 7.73 % for 4-nitrothiophenol, demonstrating its good SERS sensitivity and uniformity, and also possessed good storage stability for one month. In quantifying fenthion and methyl parathion in standard solutions and apple juice in the range of 0.05/0.02-20 mg/L, it showed LODs of 3.02 × 10-3 mg/L and 1.43 × 10-3 mg/L, and 0.0407 and 0.0075 mg/L, respectively, demonstrating potentials in ultrasensitive trace detection of pesticides in food.

Fast real-time monitoring of meat freshness based on fluorescent sensing array and deep learning: From development to deployment.

A fluorescent sensor array (FSA) combined with deep learning (DL) techniques was developed for meat freshness real-time monitoring from development to deployment. The array was made up of copper metal nanoclusters (CuNCs) and fluorescent dyes, having a good ability in the quantitative and qualitative detection of ammonia, dimethylamine, and trimethylamine gases with a low limit of detection (as low as 131.56 ppb) in range of 5 âˆ¼ 1000 ppm and visually monitoring the freshness of various meats stored at 4 °C. Moreover, SqueezeNet was applied to automatically identify the fresh level of meat based on FSA images with high accuracy (98.17 %) and further deployed in various production environments such as personal computers, mobile devices, and websites by using open neural network exchange (ONNX) technique. The entire meat freshness recognition process only takes 5 âˆ¼ 7 s. Furthermore, gradient-weighted class activation mapping (Grad-CAM) and uniform manifold approximation and projection (UMAP) explanatory algorithms were used to improve the interpretability and transparency of SqueezeNet. Thus, this study shows a new idea for FSA assisted with DL in meat freshness intelligent monitoring from development to deployment.

Terahertz spectra reconstructed using convolutional denoising autoencoder for identification of rice grains infested with Sitophilus oryzae at different growth stages.

Rice grains are often infected by Sitophilus oryzae due to improper storage, resulting in quality and quantity losses. The efficacy of terahertz time-domain spectroscopy (THz-TDS) technology in detecting Sitophilus oryzae at different stages of infestation in stored rice was employed in the current research. Terahertz (THz) spectra for rice grains infested by Sitophilus oryzae at different growth stages were acquired. Then, the convolutional denoising autoencoder (CDAE) was used to reconstruct THz spectra to reduce the noise-to-signal ratio. Finally, a random forest classification (RFC) model was developed to identify the infestation levels. Results showed that the RFC model based on the reconstructed second-order derivative spectrum with an accuracy of 84.78%, a specificity of 86.75%, a sensitivity of 86.36% and an F1-score of 85.87% performed better than the original first-order derivative THz spectrum with an accuracy of 89.13%, a specificity of 91.38%, a sensitivity of 88.18% and an F1-score of 89.16%. In addition, the convolutional layers inside the CDAE were visualized using feature maps to explain the improvement in results, illustrating that the CDAE can eliminate noise in the spectral data. Overall, THz spectra reconstructed with the CDAE provided a novel method for effective THz detection of infected grains.

A multi-label transformer-based deep learning approach to predict focal visual field progression.

PURPOSE: Tracking functional changes in visual fields (VFs) through standard automated perimetry remains a clinical standard for glaucoma diagnosis. This study aims to develop and evaluate a deep learning (DL) model to predict regional VF progression, which has not been explored in prior studies. METHODS: The study included 2430 eyes of 1283 patients with four or more consecutive VF examinations from the baseline. A multi-label transformer-based network (MTN) using longitudinal VF data was developed to predict progression in six VF regions mapped to the optic disc. Progression was defined using the mean deviation (MD) slope and calculated for all six VF regions, referred to as clusters. Separate MTN models, trained for focal progression detection and forecasting on various numbers of VFs as model input, were tested on a held-out test set. RESULTS: The MTNs overall demonstrated excellent macro-average AUCs above 0.884 in detecting focal VF progression given five or more VFs. With a minimum of 6 VFs, the model demonstrated superior and more stable overall and per-cluster performance, compared to 5 VFs. The MTN given 6 VFs achieved a macro-average AUC of 0.848 for forecasting progression across 8 VF tests. The MTN also achieved excellent performance (AUCs ≥ 0.86, 1.0 sensitivity, and specificity ≥ 0.70) in four out of six clusters for the eyes already with severe VF loss (baseline MD ≤ - 12 dB). CONCLUSION: The high prediction accuracy suggested that multi-label DL networks trained with longitudinal VF results may assist in identifying and forecasting progression in VF regions.

Regulating bacterial biofilms in food and biomedicine: unraveling mechanisms and Innovating strategies.

Bacterial biofilm has brought a lot of intractable problems in food and biomedicine areas. Conventional biofilm control mainly focuses on inactivation and removal of biofilm. However, with robust construction and enhanced resistance, the established biofilm is extremely difficult to eradicate. According to the mechanism of biofilm development, biofilm formation can be modulated by intervening in the key factors and regulatory systems. Therefore, regulation of biofilm formation has been proposed as an alternative way for effective biofilm control. This review aims to provide insights into the regulation of biofilm formation in food and biomedicine. The underlying mechanisms for early-stage biofilm establishment are summarized based on the key factors and correlated regulatory networks. Recent developments and applications of novel regulatory strategies such as anti/pro-biofilm agents, nanomaterials, functionalized surface materials and physical strategies are also discussed. The current review indicates that these innovative methods have contributed to effective biofilm control in a smart, safe and eco-friendly way. However, standard methodology for regulating biofilm formation in practical use is still missing. As biofilm formation in real-world systems could be far more complicated, further studies and interdisciplinary collaboration are still needed for simulation and experiments in the industry and other open systems.

Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus images.

AIM: To summarize the application of deep learning in detecting ophthalmic disease with ultrawide-field fundus images and analyze the advantages, limitations, and possible solutions common to all tasks. METHODS: We searched three academic databases, including PubMed, Web of Science, and Ovid, with the date of August 2022. We matched and screened according to the target keywords and publication year and retrieved a total of 4358 research papers according to the keywords, of which 23 studies were retrieved on applying deep learning in diagnosing ophthalmic disease with ultrawide-field images. RESULTS: Deep learning in ultrawide-field images can detect various ophthalmic diseases and achieve great performance, including diabetic retinopathy, glaucoma, age-related macular degeneration, retinal vein occlusions, retinal detachment, and other peripheral retinal diseases. Compared to fundus images, the ultrawide-field fundus scanning laser ophthalmoscopy enables the capture of the ocular fundus up to 200° in a single exposure, which can observe more areas of the retina. CONCLUSION: The combination of ultrawide-field fundus images and artificial intelligence will achieve great performance in diagnosing multiple ophthalmic diseases in the future.

Assembly-Induced Emission of Copper Nanoclusters: Revealing the Sensing Mechanism for Detection of Volatile Basic Nitrogen in Seafood Freshness On-Site Monitoring.

Total volatile basic nitrogen (TVB-N) is a vital indicator for assessing seafood freshness and edibility. Rapid on-site detection of volatile basic nitrogen (VBN) is of significant importance for food safety monitoring. In this study, highly luminescent self-assembled copper nanoclusters (Cu NCs@p-MBA), synthesized using p-mercaptobenzoic acid (p-MBA) as the ligand, were utilized for the sensitive detection of VBNs. Under acidic conditions, Cu NCs@p-MBA formed compact and well-organized nanosheets through noncovalent interactions, accompanied by intense orange fluorescence emission (651 nm). The benzene carboxylic acid part of Cu NCs@p-MBA provided the driving force for supramolecular assembly and exhibited a strong affinity for amines, particularly low-molecular-weight amines such as ammonia (NH3) and trimethylamine (TMA). The quantitative determination of NH3 and TMA showed the detection limits as low as 0.33 and 0.81 ppm, respectively. Cu NCs@p-MBA also demonstrated good responsiveness to putrescine and histamine. Through density functional theory (DFT) calculations and molecular dynamics (MD) simulations, the precise atomic structure, assembly structure, luminescent properties, and reaction processes of Cu NCs@p-MBA were studied, revealing the sensing mechanism of Cu NCs@p-MBA for highly sensitive detection of VBNs. Based on the self-assembled Cu NCs@p-MBA nanosheets, portable fluorescent labels were developed for semiquantitative, visual, and real-time monitoring of seafood freshness. Therefore, this study exemplified the high sensitivity of self-assembly induced emission (SAIE)-type Cu NCs@p-MBA for VBNs sensing, offering an efficient solution for on-site monitoring of seafood freshness.

Unveiling microwave and Roasting-Steam heating mechanisms in regulating fat changes in pork using cell membrane simulation.

Fat reduction due to heating or cooking is an important issue in a healthy diet. In the current study, pork subcutaneous back fat was treated via microwave heating (MH) within 10-90 s and roasting - steam heating (RSH) within 2-30 min and their dynamic changes of individual adipocytes were explored by using vesicles as a bio-membrane model. The result showed that MH and RSH significantly increased fat loss (P < 0.05), with the maximum losses being 74.1 % and 65.6 %, respectively. The mechanical strength of connective tissue decreased and then increased slightly. The microstructure demonstrated that MH and RSH treatments facilitated a large outflow of fat, showing that the particle size of the vesicle and individual adipocytes increased and then decreased. It is thus feasible to study the dynamic changes of individual adipocytes in regulating fat reduction using cell membrane simulation.

Visible detection of chilled beef freshness using a paper-based colourimetric sensor array combining with deep learning algorithms.

This study developed an innovative approach that combines a colourimetric sensor array (CSA) composed of twelve pH-response dyes with advanced algorithms, aiming to detect amine gases and assess the freshness of chilled beef. With the assistance of multivariate statistical analysis, the sensor array can effectively distinguish five amine gases and enable rapid quantification of trimethylamine vapour with a limit of detection (LOD) of 8.02 ppb and visually monitor the fresh levels of chilled beef. Moreover, the utilization of deep learning models (ResNet34, VGG16, and GoogleNet) for chilled beef freshness evaluation achieved an overall accuracy of 98.0 %. Furthermore, t-distributed stochastic neighbour embedding (t-SNE) visualized the feature extraction process and provided explanations to understand the classification process of deep learning. The results demonstrated that applying deep learning techniques in the process of pattern recognition of CSA can help in realizing the rapid, robust, and accurate assessment of chilled beef freshness.