This report outlines the development and implementation of a Computer Numerical Control (CNC) based Automated Media Dispensing System (AMDS) in a plant biotechnology research laboratory. The AMDS, a dual-channel liquid dispensing system, underwent comprehensive testing and refinement over a two-year period. Designed to serve the production demand for a commercial transformation pipeline, the system represents an initial automation step to replace manual media dispensing operations. Performance evaluation demonstrates that this iteration surpasses manual dispensing metrics, achieving a process capability index (Cpk) of 2.28 compared to 1.43 for manual operations, while maintaining comparable time per tray efficiency. The system delivers significant ergonomic benefits by reducing prolonged standing periods associated with manual dispensing tasks. Building upon established automated dispensing principles, the custom-built AMDS achieves core functionality while reducing the barrier to entry, with an estimated material cost of approximately one-fiftieth that of more comprehensive market solutions. The system's modular architecture facilitates future modifications and adaptations, positioning it as a valuable tool for addressing evolving research needs in plant biotechnology while enabling broader adoption of automated dispensing technology.

Brain tumors pose a significant risk to human life, making accurate grading essential for effective treatment planning and improved survival rates. Magnetic Resonance Imaging (MRI) plays a crucial role in this process. The objective of this study was to develop an automated brain tumor grading system utilizing deep learning techniques. A dataset comprising 293 MRI scans from patients was obtained from the Department of Radiology at Bahawal Victoria Hospital in Bahawalpur, Pakistan. The proposed approach integrates a specialized Convolutional Neural Network (CNN) with pre-trained models to classify brain tumors into low-grade (LGT) and high-grade (HGT) categories with high accuracy. To assess the model's robustness, experiments were conducted using various methods: (1) raw MRI slices, (2) MRI segments containing only the tumor area, (3) feature-extracted slices derived from the original images through the proposed CNN architecture, and (4) feature-extracted slices from tumor area-only segmented images using the proposed CNN. The MRI slices and the features extracted from them were labeled using machine learning models, including Support Vector Machine (SVM) and CNN architectures based on transfer learning, such as MobileNet, Inception V3, and ResNet-50. Additionally, a custom model was specifically developed for this research. The proposed model achieved an impressive peak accuracy of 99.45 %, with classification accuracies of 99.56 % for low-grade tumors and 99.49 % for high-grade tumors, surpassing traditional methods. These results not only enhance the accuracy of brain tumor grading but also improve computational efficiency by reducing processing time and the number of iterations required.

This special issue of SLAS Technology highlights bio-inspired computing and machine learning analytics for mental well-being in life sciences innovation. It highlights the emerging behavioral health technologies integrating big biological data, artificial intelligence, cloud/fog computing, machine learning, bio-inspired computation, the Internet of Medical Things, wireless technologies, security and privacy, semantic database, and content. The large volume of behavioral health data, often referred to as big data, requires innovative and scalable machine learning, deep learning, and intelligent algorithms to process it effectively.

With the intent of assisting gastroenterologists from all over the world, the proposed work aims to eliminate the effort required to achieve accurate diagnoses. Statistically, gastrointestinal diseases often result in fatal disorders, contributing to a significant number of fatalities. The upper gastrointestinal tract (GIT) includes the stomach, esophagus, and duodenum, while the lower one comprises a section of the small intestine, namely the ileum, as well as the large intestine, including the colon. The challenges associated with GIT tract issues are apparently complex. Therefore, multiple challenges exist regarding CAD (Computer-aided diagnosis) and endoscopy, including a lack of annotated images, a dark background, poor contrast, and an irregular pattern. The objective of this research is to develop a robust deep network, called SNet, that offers a solution to complex classification problems. Firstly, the endoscopic images undergo preprocessing before being subjected to feature extraction. This step involves image resizing along with the augmentation step. The proposed convolutional neural network (CNN) model is comprised of six blocks placed at different layers. To enable the exhaustive evaluation of proposed framework across different datasets, the model has undergone training on a very complex HyperKvasir dataset, and later tested on Kvasir v1 and v2 datasets. This facilitates cross-dataset system evaluation, resulting in an efficient system for an unseen image diagnosis. To avoid the problem of "curse of dimensionality", the most discriminant feature information is selected based on proposed minimum redundancy maximum relevance (MRMR) algorithm. The proposed architecture has been evaluated using a range of performance metrics, such as accuracy, sensitivity, specificity, and Area under curve (AUC). With classification accuracy as the main metric, the achieved accuracy is 98.45% on the Kvasir v1, and 97.83% on the Kvasir v2 datasets.

To investigate the predictive value of chest computed tomography (CT) combined with peripheral blood CD4/CD8 in patients with cerebral infarction complicated with pulmonary infection. Lung consolidation, tree and bud sign, focus calcification ratio, C-reactive protein (CRP), procalcitonin (PCT), and interleukin-6 (IL-6) were significantly higher in the infected group than in the non-infected group, and CD4 and CD4/CD8 were significantly lower than in the non-infected group (P < 0.05). The results of stratified regression analysis showed that CRP, PCT, IL-6, lung consolidation, tree and bud sign, and calcification all had significant negative effects on CD4/CD8 (t=-5.875, -3.441, -10.406, -7.741, -3.977, -6.547, all P < 0.05). Lung consolidation, tree and bud signs, calcifications, elevated CRP, elevated PCT, and elevated IL-6 were risk factors for patients with pulmonary infection, and increased CD4/CD8 was a protective factor (P < 0.05). There was a non-linear dose-response relationship between CD4/CD8 and the risk of concurrent pulmonary infection (Pnon-linearity=0.037), with a cut-off value of 0.98. The sensitivity, specificity, positive predictive value, and negative predictive value of combined diagnosis were significantly higher than CD4/CD8 (χ2=6.098, 4.640, 4.643, 6.076, P = 0.014, 0.031, 0.031, 0.014), and the area under the ROC curve of combined diagnosis was significantly higher than chest CT and peripheral blood CD4/CD8 (Z = 4.018, 5.112, P = 0.046, 0.037). Thoracic CT combined with peripheral blood CD4/CD8 can improve the diagnostic efficiency of cerebral infarction patients complicated with pulmonary infection and provide reference for clinical diagnosis and treatment.

Sleep Disordered Breathing (SDB), including conditions like Obstructive Sleep Apnea (OSA), represents a major health concern, characterized by irregular airflow during sleep due to airway obstruction. SDB can result in serious health problems. Implementation of early intervention is vital whenever patient outcomes are to be considered. This research aims to advance research on otolaryngology using Machine Learning (ML) models, and Large Language Models (LLM) for identification of SDB using Electronic Health Record (HER). The approach proposes a hybrid ML framework combining the Dynamic Seagull Search algorithm-driven Large Language model (DSS-LLM). The extensive clinical dataset is used to train the model. It includes patient demographics, medical history, sleep habits, comorbidities, and physical measurements. Data pre-processing involves handling missing values, applying NLP techniques, and normalization. Feature extraction is done using Principal Component Analysis (PCA) to reduce the dimensionality of the hyperparameters and finally for selecting the best set of predictors. The extracted features are then used to train the proposed DSS-LLM model, which incorporates the DSS algorithm to optimize the LLM classifier, improving classification accuracy and model robustness. Subsequently, the idea of LLM is introduced for its application on textual clinical records comprising physicians' reports and patients' symptoms. The findings from an experiment suggest that the proposed model enhances the classification accuracy achieved to 98.91 %, precision attained by 98.9 %, recall achieved to 98.92 % and F-1 score attained by 98.58 % as compared to the models developed earlier. This research provides a novel solution to the screening of OSA at the pre-clinical level which involves hybrid machine learning models integrated with LLMs. This proposed framework is expected to boost clinical judgment and thereby increase better ophthalmology outcomes for patients.

Lung cancer is one of the most prevalent and lethal malignant tumors worldwide. Currently, clinical diagnosis primarily relies on chest X-ray examinations, histopathological analysis, and the detection of tumor markers in blood. However, each of these methods has inherent limitations. The current study aims to explore novel diagnostic approaches for lung cancer by employing attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy in conjunction with multiple machine learning models. Fourier transform infrared spectroscopy can detect subtle differences in the material structures that reflect the carcinogenic process between lung cancer tissues and normal tissues. By applying principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to analyze infrared spectral data, these subtle differences can be amplified. The study revealed that the combination of spectral bands within the 3500-3000 cm-1 and 1600-1500 cm-1 ranges is particularly significant for differentiating between the two groups. Three classification models-Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Linear Discriminant Analysis (LDA)-were constructed for spectral analysis of various band combinations. The results indicated that in detecting lung cancer samples, the combination of the 3500-3000 cm-1 and 1600-1500 cm-1 bands offers significant advantages. The analysis of the receiver operating characteristic (ROC) curve demonstrated that the area under the curve (AUC) exceeded 0.95 for all models, with the LDA model achieving an accuracy rate of 99.4% in identifying lung cancer patients compared to healthy individuals. The findings suggest that the integration of ATR-FTIR spectroscopy with multiple machine learning models represents a promising auxiliary diagnostic method for clinical lung cancer diagnosis, enabling detection at the molecular level.