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Imaging of microscopic objects is of fundamental importance, especially in life sciences. Recent fast progress in electronic detection and control, numerical computation, and digital image processing, has been crucial in advancing modern microscopy. Digital holography is a new field in three-dimensional imaging. Digital reconstruction of a hologram offers the remarkable capability to refocus at different depths inside a transparent or semi-transparent object. Thus, this technique is very suitable for biological cell studies in vivo and could have many biomedical and biological applications. A comprehensive review of the research carried out in the area of digital holographic microscopy (DHM) for live-cell imaging is presented. The novel microscopic technique is non-destructive and label-free and offers unmatched imaging capabilities for biological and bio-medical applications. It is also suitable for imaging and modelling of key metabolic processes in living cells, microbial communities or multicellular plant tissues. Live-cell imaging by DHM allows investigation of the dynamic processes underlying the function and morphology of cells. Future applications of DHM can include real-time cell monitoring in response to clinically relevant compounds. The effect of drugs on migration, proliferation, and apoptosis of abnormal cells is an emerging field of this novel microscopic technique. Full article

Background/Objectives: High-grade gliomas are the most common primary malignant brain tumors in adults. These neoplasms remain predominantly incurable due to the genetic diversity within each tumor, leading to varied responses to specific drug therapies. With the advent of new targeted and immune therapies, which have demonstrated promising outcomes in clinical trials, there is a growing need for image-based techniques to enable early prediction of treatment response. This study aimed to evaluate the potential of radiomics and artificial intelligence implementation in predicting progression-free survival (PFS) in patients with highest-grade glioma (CNS WHO 4) undergoing a standard treatment plan. Methods: In this retrospective study, prediction models were developed in a cohort of 51 patients with pathologically confirmed highest-grade glioma (CNS WHO 4) from the authors’ institution and the repository of the Cancer Imaging Archive (TCIA). Only patients with confirmed recurrence after complete tumor resection with adjuvant radiotherapy and chemotherapy with temozolomide were included. For each patient, 109 radiomic features of the tumor were obtained from a preoperative magnetic resonance imaging (MRI) examination. Four clinical features were added manually—sex, weight, age at the time of diagnosis, and the lobe of the brain where the tumor was located. The data label was the time to recurrence, which was determined based on follow-up MRI scans. Artificial intelligence algorithms were built to predict PFS in the training set (n = 75%) and then validate it in the test set (n = 25%). The performance of each model in both the training and test datasets was assessed using mean absolute percentage error (MAPE). Results: In the test set, the random forest model showed the highest predictive performance with 1-MAPE = 92.27% and a C-index of 0.9544. The decision tree, gradient booster, and artificial neural network models showed slightly lower effectiveness with 1-MAPE of 88.31%, 80.21%, and 91.29%, respectively. Conclusions: Four of the six models built gave satisfactory results. These results show that artificial intelligence models combined with radiomic features could be useful for predicting the progression-free survival of high-grade glioma patients. This could be beneficial for risk stratification of patients, enhancing the potential for personalized treatment plans and improving overall survival. Further investigation is necessary with an expanded sample size and external multicenter validation. Full article

The World Health Organization (WHO) cancer agency predicts that more than 35 million cases of cancer will be experienced in 2050, a 77% increase over the 2022 estimate. Currently, the main cancers diagnosed are breast, lung, and colorectal. There is no standardized tool for cancer diagnoses; initially, clinical procedures are guided by the patient symptoms and usually involve biochemical blood tests, imaging, and biopsy. Label-free non-linear optical approaches are promising tools for tumor imaging, due to their inherent non-invasive biosafe contrast mechanisms and the ability to monitor collagen-related disorders, and biochemical and metabolic changes during cancer progression. In this review, the main non-linear microscopy techniques are discussed, according to three main contrast mechanisms: biochemical, metabolic, and structural imaging. Full article

Electrochemical impedance spectroscopy (EIS) is a non-invasive and label-free method widely used for characterizing cell cultures and monitoring their structure, behavior, proliferation and viability. Microfluidic systems are often used in combination with EIS methods utilizing small dimensions, controllable physicochemical microenvironments and offering rapid real-time measurements. In this work, an electrode array capable of conducting EIS measurements was integrated into a multichannel microfluidic chip which is able to trap individual cells or cell populations in specially designed channels comparable to the size of cells. An application-specific printed circuit board (PCB) was designed for the implementation of the impedance measurement in order to facilitate connection with the device used for taking EIS spectra and for selecting the channels to be measured. The PCB was designed in consideration of the optical screening of trapped cells in parallel with the EIS measurements which allows the comparison of EIS data with optical signals. With continuous EIS measurement, the filling of channels with cell suspension can be followed. Yeast cells were trapped in the microfluidic system and EIS spectra were recorded considering each individual channel, which allows differentiating between the number of trapped cells. Full article

Prostate cancer (PCa) remains a significant health threat, with chemoresistance and recurrence posing major challenges despite advances in treatment. The epithelial to mesenchymal transition (EMT), a biochemical process where cells lose epithelial features and gain mesenchymal traits, is linked to chemoresistance and metastasis. Electrical impedance spectroscopy (EIS), a novel label-free electrokinetic technique, offers promise in detecting cell phenotype changes. In this study, we employed EIS to detect EMT in prostate cancer cells (PCCs). PC3, DU145, and LNCaP cells were treated with EMT induction media for five days. EIS characterization revealed unique impedance spectra correlating with metastatic potential, distinguishing DU145 EMT+ and EMT− cells, and LNCaP EMT+ and EMT− cells (in combination with dielectrophoresis), with comparisons made to epithelial and mesenchymal controls. These changes were supported by shifts in electrical signatures, morphologies, and protein expression, including the downregulation of E-cadherin and upregulation of vimentin. No phenotype change was observed in PC3 cells, which maintained a mesenchymal phenotype. EMT+ cells were also distinguishable from mixtures of EMT+ and EMT− cells. This study demonstrates key advancements: the application of EIS and dielectrophoresis for label-free EMT detection in PCCs, characterization of cell electrical signatures after EMT, and EIS sensitivity to EMT transitions. Detecting EMT in PCa is important to the development of more effective treatments and overcoming the challenges of chemoresistance. Full article

Current deep learning models for time series often face challenges with generalizability in scenarios characterized by limited samples or inadequately labeled data. By tapping into the robust generative capabilities of diffusion models, which have shown success in computer vision and natural language processing, we see potential for improving the adaptability of deep learning models. However, the specific application of diffusion models in generating samples for time series classification tasks remains underexplored. To bridge this gap, we introduce the MDGPS model, which incorporates multi-task diffusion learning and gradient-free patch search (MDGPS). Our methodology aims to bolster the generalizability of time series classification models confronted with restricted labeled samples. The multi-task diffusion learning module integrates frequency-domain classification with random masked patches diffusion learning, leveraging frequency-domain feature representations and patch observation distributions to improve the discriminative properties of generated samples. Furthermore, a gradient-free patch search module, utilizing the particle swarm optimization algorithm, refines time series for specific samples through a pre-trained multi-task diffusion model. This process aims to reduce classification errors caused by random patch masking. The experimental results on four time series datasets show that the proposed MDGPS model consistently surpasses other methods, achieving the highest classification accuracy and F1-score across all datasets: 95.81%, 87.64%, 82.31%, and 100% in accuracy; and 95.21%, 82.32%, 78.57%, and 100% in F1-Score for Epilepsy, FD-B, Gesture, and EMG, respectively. In addition, evaluations in a reinforcement learning scenario confirm MDGPS’s superior performance. Ablation and visualization experiments further validate the effectiveness of its individual components. Full article

The geometric shape, symmetry, and topology of colloidal particles often allow for controlling colloidal phase behavior and physical properties of these soft matter systems. In liquid crystalline dispersions, colloidal particles with low symmetry and nontrivial topology of surface confinement are of particular interest, including surfaces shaped as handlebodies, spirals, knots, multi-component links, and so on. These types of colloidal surfaces induce topologically nontrivial three-dimensional director field configurations and topological defects. Director switching by electric fields, laser tweezing of defects, and local photo-thermal melting of the liquid crystal host medium promote transformations among many stable and metastable particle-induced director configurations that can be revealed by means of direct label-free three-dimensional nonlinear optical imaging. The interplay between topologies of colloidal surfaces, director fields, and defects is found to show a number of unexpected features, such as knotting and linking of line defects, often uniquely arising from the nonpolar nature of the nematic director field. This review article highlights fascinating examples of new physical behavior arising from the interplay of nematic molecular order and both chiral symmetry and topology of colloidal inclusions within the nematic host. Furthermore, the article concludes with a brief discussion of how these findings may lay the groundwork for new types of topology-dictated self-assembly in soft condensed matter leading to novel mesostructured composite materials, as well as for experimental insights into the pure-math aspects of low-dimensional topology. Full article

The plant vascular system is not only a transportation system for delivering nutrients but also a highway transport network for spreading viruses. Tomato spotted wilt orthotospovirus (TSWV) is among the most destructive viruses that cause serious losses in economically important crops worldwide. However, there is minimal information about the long-distance movements of TSWV in the host plant vascular system. In this this study, we confirm that TSWV virions are present in the xylem as observed by transmission electron microscopy (TEM). Further, a quantitative proteomic analysis based on label-free methods was conducted to reveal the uniqueness of protein expression in xylem sap during TSWV infection. Thus, this study identified and quantified 3305 proteins in two groups. Furthermore, TSWV infection induced three viral structural proteins, N, Gn and Gc, and 315 host proteins differentially expressed in xylem (163 up-regulated and 152 down-regulated). GO enrichment analysis showed up-regulated proteins significantly enriched in homeostasis, wounding, defense response, and DNA integration terms, while down-regulated proteins significantly enriched in cell wall biogenesis/xyloglucan metabolic process-related terms. KEGG enrichment analysis showed that the differentially expressed proteins (DEPs) were most strongly associated with plant-pathogen interaction, MAPK signaling pathway, and plant hormone signal transduction. Cluster analysis of DEPs function showed the DEPs can be categorized into cell wall metabolism-related proteins, antioxidant proteins, PCD-related proteins, host defense proteins such as receptor-like kinases (RLKs), salicylic acid binding protein (SABP), pathogenesis related proteins (PR), DNA methylation, and proteinase inhibitor (PI). Finally, parallel reaction monitoring (PRM) validated 20 DEPs, demonstrating that the protein abundances were consistent between label-free and PRM data. Finally, 11 genes were selected for RT-qPCR validation of the DEPs and label-free-based proteomic analysis concordant results. Our results contribute to existing knowledge on the complexity of host plant xylem system response to virus infection and provide a basis for further study of the mechanism underlying TSWV long-distance movement in host plant vascular system. Full article

Mycoplasma bovis is a global problem for the cattle industry due to its high infection rates and associated morbidity, although its pathophysiology is poorly understood. In this study, the M. bovis transcriptome and proteome were analyzed to further investigate the biology of clinical isolates of M. bovis. A differential analysis of M. bovis, a clinical isolate (NX114), and an international type strain (PG45) at the logarithmic stage of growth, was carried out using prokaryotic transcriptome and 4D-label-free quantitative non-labeled proteomics. Transcriptomics and proteomics identified 193 DEGs and 158 DEPs, respectively, with significant differences in 49 proteins/34 transcriptomic CDS post-translational protein sequences (15 jointly up-regulated and 21 jointly down-regulated). GO comments indicate membrane, cytoplasmic and ribosome proteins were important components of the total proteins of M. bovis NX114 clinical isolate. KEGG enrichment revealed that M. bovis NX114 is mainly associated with energy metabolism, the biosynthesis of secondary metabolites, and the ABC transporters system. In addition, we annotated a novel adhesion protein that may be closely related to M. bovis infection. Triosephosphate isomerase (TpiA) and Pyruvate kinase (Pyk) genes may be the key enzymes that regulate the growth and maintenance of M. bovis and are involved in the pathogenic process as virulence factors. The results of the study revealed the biology of different isolates of M. bovis and may provide research ideas for the pathogenic mechanism of M. bovis. Full article

Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light and thermal infrared images was established for analyses, including morphological, thermographic, comb, and behavioral assessments, enabling a comprehensive evaluation of the hens’ health, behavior, and population counts. To address the issue of insufficient data samples in the health detection process for individual and group hens, a dataset named BClayinghens was constructed containing 61,133 images of visible light and thermal infrared images. The BClayinghens dataset was completed using three types of devices: smartphones, visible light cameras, and infrared thermal cameras. All thermal infrared images correspond to visible light images and have achieved positional alignment through coordinate correction. Additionally, the visible light images were annotated with chicken head labels, obtaining 63,693 chicken head labels, which can be directly used for training deep learning models for chicken head object detection and combined with corresponding thermal infrared data to analyze the temperature of the chicken heads. To enable the constructed deep-learning object detection and recognition models to adapt to different breeding environments, various data enhancement methods such as rotation, shearing, color enhancement, and noise addition were used for image processing. The BClayinghens dataset is important for applying visible light images and corresponding thermal infrared images in the health detection, behavioral analysis, and counting of caged laying hens under large-scale farming. Full article

Cellular heterogeneity plays a significant role in understanding biological processes, such as cell cycle and disease progression. Microfluidics has emerged as a versatile tool for manipulating single cells and analyzing their heterogeneity with the merits of precise fluid control, small sample consumption, easy integration, and high throughput. Specifically, integrating microfluidics with electrical techniques provides a rapid, label-free, and non-invasive way to investigate cellular heterogeneity at the single-cell level. Here, we review the recent development of microfluidic-based electrical strategies for single-cell manipulation and analysis, including dielectrophoresis- and electroporation-based single-cell manipulation, impedance- and AC electrokinetic-based methods, and electrochemical-based single-cell detection methods. Finally, the challenges and future perspectives of the microfluidic-based electrical techniques for single-cell analysis are proposed. Full article

Giant pandas are an endangered species with low reproductive rates. Cryptorchidism, which can negatively affect reproduction, is also often found in pandas. Seminal plasma plays a crucial role in sperm–environment interactions, and its properties are closely linked to conception potential in both natural and assisted reproduction. The research sought to identify seminal fluid protein content variations between normal and cryptorchid giant pandas. Methods: Using a label-free MS-based method, the semen proteomes of one panda with cryptorchidism and three normal pandas were studied, and the identified proteins were compared and functionally analyzed. Results: Mass spectrometry identified 2059 seminal plasma proteins, with 361 differentially expressed proteins (DEPs). Gene ontology (GO) analysis revealed that these DEPs are mainly involved in the phosphate-containing compound metabolic, hydrolase activity, and kinase activity areas (p ≤ 0.05). The KEGG functional enrichment analysis revealed that the top 20 pathways were notably concentrated in the adipocyte lipolysis and insulin metabolism pathway, with a significance level of p ≤ 0.05. Further analysis through a protein–protein interaction (PPI) network identified nine key proteins that may play crucial roles, including D2GXH8 (hexokinase Fragment), D2HSQ6 (protein tyrosine phosphatase), and G1LHZ6 (Calmodulin 2). Conclusions: We suspect that the high abundance of D2HSQ6 in cryptorchid individuals is associated with metabolic pathways, especially the insulin signal pathway, as a typical proteomic feature related to its pathological features. These findings offer insight into the ex situ breeding conditions of this threatened species. Full article

Cardiac troponin I (cTnI) monitoring is of great value in the clinical diagnosis of acute myocardial infarction (AMI). In this paper, a highly sensitive electrochemical aptamer sensor using polystyrene (PS) microspheres as the electrode substrate material in combination with Prussian blue (PB) and gold nanoparticles (AuNPs) was demonstrated for the sensitive and label-free determination of cTnI. PS microspheres were synthesized by emulsion polymerization and then dropped onto the glassy carbon electrode (GCE); PB and AuNPs were electrodeposited on the electrode in corresponding electrolyte solutions step by step. The PS microsphere substrate provided a large surface area for the loading mass of the biological affinity aptamers, while the PB layer improved the electrical conductivity of the modified electrode, and the electroactive AuNPs exhibited excellent catalytic performance for the subsequent electrochemical measurements. In view of the above mentioned AuNPs/PB/PS/GCE sensing platform, the fabricated label-free electrochemical aptamer sensor exhibited a wide detection range of 10 fg/mL~1.0 μg/mL and a low detection limit of 2.03 fg/mL under the optimal conditions. Furthermore, this biosensor provided an effective detection platform for the analysis of cTnI in serum samples. The introduction of this sensitive electrochemical aptamer sensor provides a reference for clinically sensitive detection of cTnI. Full article

Chloramphenicol (CAM), a well-known broad-spectrum antibiotic, inhibits peptide bond formation in bacterial ribosomes. It has been reported to affect ribosome assembly mainly through disrupting the balance of ribosomal proteins. The present study investigates the multifaceted effects of CAM on the maturation of the 50S ribosomal subunit in Escherichia coli (E. coli). Using label-free quantitative mass spectrometry (LFQ-MS), we observed that CAM treatment also leads to the upregulation of assembly factors. Further cryo-electron microscopy (cryo-EM) analysis of the ribosomal precursors characterized the CAM-treatment-accumulated pre-50S intermediates. Heterogeneous reconstruction identified 26 distinct pre-50S intermediates, which were categorized into nine main states based on their structural features. Our structural analysis highlighted that CAM severely impedes the formation of the central protuberance (CP), H89, and H58 during 50S ribosomal subunit maturation. The ELISA assay further demonstrated the direct binding of CAM to the ribosomal precursors, suggesting that the interference with 50S maturation occurs through a combination of direct and indirect mechanisms. These findings provide new insights into the mechanism of the action of CAM and provide a foundation for a better understanding of the assembly landscapes of the ribosome. Full article

Optical microscopy is widely regarded to be an indispensable tool in healthcare and manufacturing quality control processes, although its inability to resolve structures separated by a lateral distance under ~200 nm has culminated in the emergence of a new field named fluorescence nanoscopy, while this too is prone to several caveats (namely phototoxicity, interference caused by exogenous probes and cost). In this regard, we present a triplet string of concatenated O-Net (‘bead’) architectures (termed ‘Θ-Net’ in the present study) as a cost-efficient and non-invasive approach to enhancing the resolution of non-fluorescent phase-modulated optical microscopical images in silico. The quality of the afore-mentioned enhanced resolution (ER) images was compared with that obtained via other popular frameworks (such as ANNA-PALM, BSRGAN and 3D RCAN), with the Θ-Net-generated ER images depicting an increased level of detail (unlike previous DNNs). In addition, the use of cross-domain (transfer) learning to enhance the capabilities of models trained on differential interference contrast (DIC) datasets [where phasic variations are not as prominently manifested as amplitude/intensity differences in the individual pixels unlike phase-contrast microscopy (PCM)] has resulted in the Θ-Net-generated images closely approximating that of the expected (ground truth) images for both the DIC and PCM datasets. This thus demonstrates the viability of our current Θ-Net architecture in attaining highly resolved images under poor signal-to-noise ratios while eliminating the need for a priori PSF and OTF information, thereby potentially impacting several engineering fronts (particularly biomedical imaging and sensing, precision engineering and optical metrology). Full article