Photoacoustic (PA) microscopy allows imaging of the soft biological tissue based on optical absorption contrast and spatial ultrasound resolution. One of the major applications of PA imaging is its characterization of microvasculature. However, the strong PA signal from skin layer overshadowed the subcutaneous blood vessels leading to indirectly reconstruct the PA images in human study. Addressing the present situation, we examined a deep learning (DL) automatic algorithm to achieve high-resolution and high-contrast segmentation for widening PA imaging applications. In this research, we propose a DL model based on modified U-Net for extracting the relationship features between amplitudes of the generated PA signal from skin and underlying vessels. This study illustrates the broader potential of hybrid complex network as an automatic segmentation tool for the in vivo PA imaging. With DL-infused solution, our result outperforms the previous studies with achieved real-time semantic segmentation on large-size high-resolution PA images.

ECG devices are increasingly used in the monitoring and timely detection of cardiovascular abnormalities. One of the biggest challenges for ECG device designers is to eliminate noise from the surrounding environment to obtain high-quality and real-time ECG signals. A microcontroller usually has low processing speed, so it is not appropriate for real-time processing of ECG signals with high sampling frequency. Processing continuous signals requires a high processing speed or processor that can be used with many partial processing hierarchies. In 2018, Microchip Technology Inc. launched a new family of 16-bit digital signal controllers (DSCs) with two cores: the master core can be operated at 180 MHz and the slave core can be operated at 200 MHz. The new DSC family is suitable for mobile application systems, such as ECG devices, which can collect and process data in real time with a process hierarchy. In this paper, we present an application of the two cores of DSCs in collecting and processing ECG signals in real time. In addition, we implement an infinite impulse response (IIR) filter to eliminate noise from the surrounding environment. Specifically, the master core collects and implements a high-pass IIR filter with five poles, and the slave core implements the low-pass IIR filter with seven poles and sampling frequency of ECG signals up to 400 Hz.

Mitochondria-associated membranes (MAMs) are essential for cellular homeostasis. MAMs are specialized contact sites located between the endoplasmic reticulum (ER) and mitochondria and control apoptotic pathways, lipid metabolism, autophagy initiation, and calcium signaling, processes critical to the survival and function of neurons. Although this area of membrane biology remains understudied, increasing evidence links MAM dysfunction to the etiology of major neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis (ALS). MAMs consist of a network of protein complexes that mediate molecular exchange and ER–mitochondria tethering. MAMs regulate lipid flow in the brain, including phosphatidylserine and cholesterol; disruption of this process causes membrane instability and impaired synaptic function. Inositol 1,4,5-trisphosphate receptor—voltage-dependent anion channel 1 (IP3R-VDAC1) interactions at MAMs maintain calcium homeostasis, which is required for mitochondria to produce ATP; dysregulation promotes oxidative stress and neuronal death. An effective therapeutic approach for altering neurodegenerative processes is to restore the functional integrity of MAMs. Improving cell-to-cell interactions and modulating MAM-associated proteins may contribute to the restoration of calcium homeostasis and lipid metabolism, both of which are key for neuronal protection. MAMs significantly contribute to the progression of neurodegenerative diseases, making them promising targets for future therapeutic research. This review emphasizes the increasing importance of MAMs in the study of neurodegeneration and their potential as novel targets for membrane-based therapeutic interventions.

The efficiency of a cancer therapy agent depends on its ability to eliminate tumors without endangering neighboring healthy tissues. In this present study, a novel multifunctional property enriched nanostructured system was synthesized on manganese-doped hydroxyapatite (Mn-HAp) conjugated with counter folic acid (FA) IR-783 fluorescence dye. The tailored synthesis of nano rod-shaped Mn-HAp nanoparticles with high surface area allows to conjugate FA/IR-783 dye which enhanced retention time during in vivo circulation. The drug-free Photothermal Photodynamic therapy mediated cancer treatment permits the prevention of collateral damages to non-cancerous cells. The safe HAp biomaterial matrix allows a large number of molecules on its surface due to its active different charge moieties (Ca²⁺/PO₄^3-) without any recurrence toxicity. The doped Mn allows releasing of Mn²⁺ ions which triggered the production of toxic hydroxyl radicals (•OH) via Fenton or Fenton-like reactions to decompose H₂O₂ in the tumor sites. Herein, IR-783 and FA were selected for targeted fluorescence imaging-guided photothermal therapy. 6The PTT performance of synthesized nanostructured system shows enhanced potential with ∼60°C temperature elevation with 0.75 W∙cm⁻² power irradiated within 7 min of treatment. PDT activity was also observed initially with Methylene Blue (MB) as a targeted material which shows a drastic degradation of MB and further in vitro studies with MDA-MB-231 breast cancer cell line show cytotoxicity due to the generated reactive oxygen species (ROS) effect. FA/IR-783 conjugated Mn-HAp nanoparticles (2.0 mol% Mn-HAp/FA-IR-783) show significant tumor-specific targeting and treatment efficiency while intravenously injected in (tail vain) BALB/c nude mice model without any recurrence. The synthesized nanostructured system had ample scope to be a promising Photo-Therapeutic agent for cancer treatment.

Redox imbalance and epigenetic dysregulation, which both contribute to tumor initiation, survival, and resistance to therapy, are intimately linked to the progression of cancer. Reactive oxygen species (ROS) have two contrasting effects: at moderate concentrations, they promote angiogenesis and oncogenic signaling, whereas at high concentrations, they trigger apoptosis. Oxidative stress alters histone modifications, DNA methylation, and non-coding RNA (ncRNA) expression, reshaping the epigenetic landscape and supporting malignant phenotypes. Plant-derived antioxidants, including flavonoids, polyphenols, alkaloids, and terpenoids, act as dual modulators of cancer biology. They scavenge or regulate reactive oxygen species (ROS), restore redox balance, activate tumor suppressor pathways, inhibit oncogenic mechanisms, and reverse abnormal epigenetic marks. Compounds such as resveratrol, curcumin, epigallocatechin gallate (EGCG), quercetin, and sulforaphane modulate DNA methyltransferases (DNMTs), histone deacetylases (HDACs), and non-coding RNA networks, and can enhance chemotherapy and radiation therapy. Despite promising mechanisms, challenges remain in translational efficacy, optimal dosing, and bioavailability. This review emphasizes the potential of plant-derived antioxidants as precision oncology adjuncts and highlights the need for biomarker-guided strategies, nano-delivery systems, and clinical validation to fully realize their therapeutic benefits. Plant-derived antioxidants mitigate ROS-induced oncogenic signaling, as evidenced by in vitro and clinical models.

Automatic Anomaly Detection (AAD) has emerged as an advanced vision-based measurement method with diverse applications in healthcare and security. However, current AAD methods still face challenges related to data limitations and labeled-data imbalances, which limit the accuracy and reliability of AAD in real-life applications. Additionally, labeling and training large datasets for video anomaly detection (VAD) is computationally demanding and time-consuming. To address these challenges, this work introduces AADC-Net, a multimodal deep neural network for automated abnormal event detection and categorization. The key contributions of this research are as follows: 1) AADC-Net leverages pre-trained large language models (LLMs) and vision-language models (VLMs) to mitigate VAD dataset limitations and imbalances. 2) A pre-trained object detection model (DETR) is integrated for visual feature extraction, eliminating the need for bounding box supervision. 3) The experimental results demonstrate the state-of-the-art (SOTA) performance of the proposed AADC-Net with an Area Under the Curve (AUC) of 83.2% and an Average Precision (AP) of 83.8% on the public UCF-Crime and XD-Violence datasets, respectively. 4) Additionally, AADC-Net can be integrated into existing video surveillance systems, such as those in smart gyms and healthcare facilities, to automatically detect anomalies in real time with minimal supervision, enhancing security, monitoring, and reducing labor costs while minimizing human error. In summary, our results demonstrate that AADC-Net not only achieves high accuracy in anomaly detection but also provides a practical solution for real-world surveillance applications.