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Alzheimer's disease (AD), the most common form of dementia, remains a leading neurodegenerative disorder that necessitates the development of diagnostic markers. While current cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers facilitate diagnostic accuracy, their invasive and pricey nature limits widespread application. Blood-based biomarkers, such as plasma Aβ42/40 and phosphorylated tau isoforms, are emerging as accessible alternatives. Biomarkers reflecting neurodegeneration (e.g. neurofilament light chain, brain-derived tau) and neuroinflammation (e.g. glial fibrillary acidic protein, TSPO-PET) provide additional insights into disease progression. Novel approaches - including exosomal and Aβ seeds biomarkers, omics techniques, and retinal imaging - further broaden the diagnostic landscape. Despite the promising perspectives, challenges remain in validity and utility. This review highlights recent advances of AD diagnostic markers, evaluates their clinical potential and limitations, and outlines future directions guided by the Geneva five-phase roadmap. The ultimate aim is to facilitate earlier detection and timely intervention of this burdensome disorder.

Energy production and use must be in balance with the ecosystem since it is a necessary part of life. Among the many renewable and non-renewable energy sources, hydrogen is considered a sustainable and green energy source. Sustainable hydrogen production will be required for the hydrogen economy, and this can be accomplished by electrochemically splitting water with efficient electrocatalysts. Because of their significant catalytic activity, low overpotential, and low energy consumption, platinum (Pt) and Pt-based catalysts are utilized. The hydrogen evolution reaction (HER), which uses pricey Pt catalysts to electrocatalytically reduce water to molecular hydrogen, has the potential to be a sustainable energy source; however, its limited availability and high cost limit its practical applicability. As a result, a simple, low-cost, stable, and environmentally friendly thiol-substituted cobalt phthalocyanine (MTCoPc) was utilized as an electrocatalyst for HER activity. MTCoPc/SP (SP: super-p) hybrid composite electrocatalyst demonstrated good HER catalytic performance in 1 M KOH, with overpotentials of 110 and 169 mV at current densities of 10 and 50 mA cm-2, respectively. The MTCoPc/SP hybrid composite's remarkable activity toward HER and long-term stability make it a potential electrocatalyst for real-time application.

DNA-binding proteins (DBPs) are fundamental to many key cellular processes, possessing distinct binding domains, differential affinities for single- and double-stranded DNA structures, and playing roles in fundamental biological functions such as DNA replication and gene regulation. They are intimately linked to the pathological mechanisms of diseases like neurodegenerative disorders and cancers, making their prediction pivotal for unraveling protein function and disease mechanisms. However, conventional experimental techniques for DBP identification are temporally inefficient, labor-intensive, material-intensive and pricey. Existing DNA-binding protein prediction models either lack integration with pre-trained protein language models, primarily relying on manually constructed features, or despite utilizing pre-trained language models, fail to extract sufficiently effective information from the features generated by these models. Hence, a pressing imperative persists to devise robust computational frameworks capable of precise and efficient DBP delineation. In this study, we propose a novel deep learning-based method named CNNCaps-DBP for the accurate prediction of DBPs from primary sequence information. Our methodology incorporates the pre-trained protein language model ESM C and enhances the embeddings via an attention augmented convolution module. The extracted features are then passed through a hybrid deep learning framework consisting of Capsule network and MLP to construct the final predictive model. To optimize the model's training process, we applied a dynamic learning rate scheduler utilized for lessening the risk of premature convergence and enhance the robustness of the learning process. Experimental results show that CNNCaps-DBP significantly outperforms previous models in terms of predictive performance. To further validate the robustness of the proposed model, we evaluated it on additional independent datasets, where CNNCaps-DBP consistently outperformed state-of-the-art methods. In addition, we conducted two case studies to interpret the predictions of our model, which demonstrates the strong predictive capability for DBP identification. The source code used in this study is available at: https://github.com/YZYAlex/CNNCaps-DBP.

Background: Chemotherapy continues to be the cornerstone for the management of leishmaniasis. The preferred medications are pricey and have a number of unfavorable side effects. These restrictions make it necessary to produce novel antileishmanial chemicals, and plants have opportunities in this respect. Objectives: This study aimed to evaluate the antileishmanial properties of Thymus syriacus essential oil and its mechanisms of action. Results: Our findings demonstrated that Thymus syriacus essential oil, rich in thymol, exhibited potent antileishmanial activity, with an IC50 value of approximately 1 µg/mL against L. tropica promastigotes. Furthermore, the cell cycle arrest at the sub-G0-G1 phase supported the theory that the leishmanicidal effect was mediated by apoptosis. Methods: The essential oil was characterized using gas chromatography-tandem mass spectrometry. Antileishmanial activity against L. tropica promastigotes was assessed, with mechanisms confirmed via flow cytometry. Conclusions: These results confirm the potential of Thymus syriacus essential oil as a promising therapeutic candidate for the treatment of leishmaniasis.

Glioblastoma (GBM), a very aggressive type of brain tumor, sometimes creates a chemoresistant state that compromises the effectiveness of chemotherapy and leads to serious clinical complications in treatment. Predicting drug resistance is crucial for the improvement of medication effect during cancer treatment. Assessing drug resistance is difficult due to the pricey chemotherapeutic trails and prolonged laboratory investigations. Deep learning plays a significant role in drug resistance prediction nowadays. This paper presents a novel deep learning model that combines Convolutional Neural Networks (CNN), Long Short Term Memory Networks (LSTM), and transformer architectures to predict drug resistance. The proposed application acts as a system that estimate the resistance of drugs based on gene expression details and chemical properties. As compared with existing model for drug resistance prediction, proposed model achieved lower Mean Squared Error (MSE) of 0.4109 and Mean Absolute Error (MAE) of 0.5040, along with higher R2 of 0.9635 and pearson correlation of 0.9999. This work significantly advances the fields of pharmacogenomics and personalized medicine through an in-depth evaluation that includes complex performance metrics and visualizations.

Oil extraction from reservoirs has never been easy, particularly when easily accessible oil sources run out. Enhanced oil recovery (EOR) is a dynamic area of petroleum engineering that seeks to maximize the quantity of crude oil that can be retrieved from an oil field. Researchers and oil producers have emphasized assessing tertiary-stage recovery approaches, such as chemical EOR (CEOR), due to the problems posed by the diverse carbonate rocks. Polymers and surfactants used in CEOR procedures have the potential to harm formation and contaminate the environment. The environmentally beneficial "green enhanced oil recovery" (GEOR) technique includes infusing green fluids to raise tertiary oil output and boost macroscopic and microscopic sweep efficiency, ensuring sustainable practices while minimizing environmental concerns. Utilizing eco-friendly carbon nanomaterials such as biomass-based carbon dots (CDs), carbon nanotubes (CNTs), graphene, and their derivatives for EOR and reservoir monitoring applications represents a promising frontier in the petroleum industry. These particles are pricey and do not extend to GEOR but have been successfully tested in EOR. This innovative approach capitalizes on the unique properties of these nanomaterials to improve the efficiency and sustainability of oil extraction processes. This review aims to explore biomass-derived carbon nanoparticles and investigate their possible functions in GEOR. Furthermore, the use of carbon particles in the GEOR approach is still poorly understood; thus, there needs to be a lot of credentials. The effectiveness, sustainability, and environmental responsibility of petroleum production operations can be enhanced by incorporating carbon nanomaterials from biomass into enhanced oil recovery systems. An environmentally friendly and more resilient energy future may be possible if research and development in this area are allowed to continue. This might completely change how oil resources are found and used.

Menaquinone-7 (MK-7) is a valuable vitamin K2 produced by Bacillus subtilis. Although many strategies have been adopted to increase the yield of MK-7 in B. subtilis, the effectiveness of these common approaches is not high because long metabolic synthesis pathways and numerous bypass pathways competing for precursors with MK-7 synthesis. Regarding the modification of bypass pathways, studies of common static metabolic engineering method such as knocking out genes involved in side pathway have been reported previously. Since byproductsphenylalanine(Phe), tyrosine (Tyr), tryptophan (Trp), folic acid, dihydroxybenzoate, hydroxybutanone in the MK-7 synthesis pathway are indispensable for cell growth, the complete knockout of the bypass pathway restricts cell growth, resulting in limited increase in MK-7 synthesis. Dynamic regulation via quorum sensing (QS) provides a cost-effective strategy to harmonize cell growth and product synthesis, eliminating the need for pricey inducers. SinR, a transcriptional repressor, is crucial in suppressing biofilm formation, a process closely intertwined with MK-7 biosynthesis. Given this link, we targeted SinR to construct a dynamic regulatory system, aiming to modulate MK-7 production by leveraging SinR's regulatory influence. A modular PhrC-RapC-SinR QS system is developed to dynamic regulate side pathway of MK-7. In this study, first, we analyzed the SinR-based gene expression regulation system in B. subtilis 168 (BS168). We constructed a promoter library of different abilities, selected suitable promoters from the library, and performed mutation screening on the selected promoters. Furthermore, we constructed a PhrC-RapC-SinR QS system to dynamically control the synthesis of Phe, Tyr, Trp, folic acid, dihydroxybenzoate, hydroxybutanone in MK-7 synthesis in BS168. Cell growth and efficient synthesis of the MK-7 production can be dynamically balanced by this QS system. Using this system to balance cell growth and product fermentation, the MK-7 yield was ultimately increased by 6.27-fold, from 13.95 mg/L to 87.52 mg/L. In summary, the PhrC-RapC-SinR QS system has been successfully integrated with biocatalytic functions to achieve dynamic metabolic pathway control in BS168, which has potential applicability to a large number of microorganisms to fine-tune gene expression and enhance the production of metabolites.

Utilizing waste materials and reclaimed asphalt pavement (RAP) in road construction is gaining research interest. The pricey nature of building roads is attributed to the energy required for new material production and its environmental impact. The study investigated rheological and chemical properties of reclaimed asphalt pavement using PET additive as a modifier. The materials used in this study are virgin bitumen (VB), RAP bitumen (RB), and PET bottles. The synthesis of PET bottle into PET-derived additives was carried out, using aminolysis method in the presence of triethylenetriamine (TETA) as a solvent. Additionally, polymer-modified bitumen (PMB) was created by adding 2% PET additive with VB. The findings showed that utilizing PMB with RB in asphalt blend improved RB's brittle effect and has high resistance to deformation at high temperature. Moreover, the effect of the PMB on the asphalt binder did not alter the functional group while there was a reduced mass loss for PMB. Consequently, PET additives can be used to improve the quality of asphalt mixture in road construction, thus enhancing sustainability.

Microelectrodes have played a crucial role in electrochemistry for the last few decades. However, the conventional lithographic processes, the key players in fabrication, are nonetheless technologically challenging, pricey, and lack reproducibility. In this work has developed a novel and low-cost patterned-replication fabrication technology for interdigitated electrode array (IDA) electrodes on the polymer substrate. Conventional UV-lithography has been utilized to fabricate the nickel IDA electrode pattern as a master mold on the stainless-steel substrate, which was replicated onto the polymer substrate by the hot-emboss technique. Then, gold was deposited on the replicated wafer by electron beam evaporation, and finally adhesive tape lift-off was used to obtain the gold IDA electrode. The fabricated IDA electrode was applied for electrochemical detection of various p-aminophenol (PAP) concentrations as a representative biomarker with a detection limit of 0.01 nM. Finally, different levels of amyloid beta 42 (Aß42) and amyloid beta aggregated (Aß Agg.), two Alzheimer's disease (AD) biomarkers, were measured using the developed IDA electrode via e-ELISA using enzyme by-products PAP. While quantified, the proposed IDA electrode successfully detects Aß42 and Aß Agg. with the lower detection limit (LOD) of 3.9 and 7.81 pg/ml, respectively.

Dental composites' dependence on pricey imported components often raises price concerns and restricts accessibility in restorative dentistry. In Pakistan, using locally produced materials presents a viable alternative for more affordable and environmentally friendly dental treatments. This study aimed to develop and evaluate dental composites sourced from locally available materials in Pakistan for cost-effectiveness, performance, and durability. A study comprising 385 patients, aged 18-65 years, who underwent composite restorations made of novel materials, was carried out at Sardar Begum Dental College, Peshawar, from January 2023 to December 2023. Patient comfort, aesthetic quality, and durability were the three main criteria used to assess clinical performance. The physical characteristics of adhesion strength, wear resistance, and hardness were evaluated in lab settings. Costs for materials, processing, and clinical application were all considered in a thorough cost study. SPSS version 26 (Armonk, NY: IBM Corp.) was used for the statistical analysis, and both descriptive and inferential statistics were used. The novel composites were compared with standard materials using t-tests, and cost differences were assessed. A p-value of less than 0.05 was considered statistically significant. The novel composites exhibited superior performance with a durability of 12.57 &#xb1; 1.83 months (n = 385 patients), compared to 11.05 &#xb1; 2.38 months for conventional composites (p < 0.05). They also scored higher in aesthetic quality (8.35 &#xb1; 1.48 vs. 7.82 &#xb1; 1.59) and patient comfort (4.52 &#xb1; 0.79 vs. 4.27 &#xb1; 0.83) (p < 0.05). Laboratory tests showed greater hardness (48.79 &#xb1; 3.22 Vickers), better wear resistance (0.28 &#xb1; 0.05 mm&#xb3;), and higher adhesion strength (22.96 &#xb1; 1.68 MPa) (p < 0.05). Cost analysis revealed significant savings, with total costs per patient of PKR 8,980 for novel composites vs. PKR 33,000 for conventional composites (p < 0.05). Novel dental composites developed from locally sourced materials demonstrate superior performance and significant cost reductions, highlighting their potential to enhance the accessibility and affordability of dental care in Pakistan.

Standard enzyme-linked immunosorbent assays based on microplates are frequently utilized for various molecular sensing, disease screening, and nanomedicine applications. Comparing this multi-well plate batched analysis to non-batched or non-standard testing, the diagnosis expenses per patient are drastically reduced. However, the requirement for rather big and pricey readout instruments prevents their application in environments with limited resources, especially in the field. In this work, a handheld cellphone-based colorimetric microplate reader for quick, credible, and novel analysis of digital images of human cancer cell lines at a reasonable price was developed. Using our in-house-developed app, images of the plates are captured and sent to our servers, where they are processed using a machine learning algorithm to produce diagnostic results. Using FDA-approved human epididymis protein of ovary IgG (HE4), prostate cancer cell line (PC3), and bladder cancer cell line (5637) ELISA tests, we successfully examined this mobile platform. The accuracies for the HE4, PC3, and 5637 tests were 93%, 97.5%, and 97.2%, respectively. By contrasting the findings with the measurements made using optical absorption EPOCH microplate readers and optical absorption Tecan microplate readers, this approach was found to be accurate and effective. As a result, digital image colorimetry on smart devices offered a practical, user-friendly, affordable, precise, and effective method for quickly identifying human cancer cell lines. Thus, healthcare providers might use this portable device to carry out high-throughput illness screening, epidemiological investigations or monitor vaccination campaigns.

Early detection and diagnosis of thyroid nodule types are important because they can be treated more effectively in their early stages. The types of thyroid nodules are generally stated as atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS), benign follicular, and papillary follicular. The risk of malignancy for AUS/FLUS is typically stated to be between 5 and 15&#xa0;%, while some studies indicate a risk as high as 25&#xa0;%. Without complete histology, it is difficult to classify nodules and these diagnostic operations are pricey and risky. To minimize laborious workload and misdiagnosis, recently various AI-based decision support systems have been developed. In this study, a novel AI-based decision support system has been developed for the automated segmentation and classification of the types of thyroid nodules. This system is based on a hybrid deep-learning procedure that makes both an automatic thyroid nodule segmentation and classification tasks, respectively. In this framework, the segmentation is executed with some U-Net architectures such as ResUNet and ResUNet++ integrating with the feature extraction and upsampling with dropout operations to prevent overfitting. The nodule classification task is achieved by various deep nets architecture such as VGG-16, DenseNet121, ResNet-50, and Inception ResNet-v2 considering some accurate classification criteria such as Intersection over Union (IOU), Dice coefficient, accuracy, precision, and recall. In analysis, a total of 880 patients with ages ranging from 10 to 90 years were included by taking the ultrasound images and demographics. The experimental evaluations showed that ResUNet++ demonstrated excellent segmentation outcomes, attaining remarkable evaluation scores including a dice coefficient of 92.4&#xa0;% and a mean IOU of 89.7&#xa0;%. ResNet-50 and Inception ResNet-v2 trained over the images segmented with UNets have shown better performance in terms of achieving high evaluation scores for the classification accuracy such as 96.6&#xa0;% and 95.0&#xa0;%, respectively. In addition, ResNet-50 and Inception ResNet-v2 classified AUS/FLUS from the images segmented with UNets with AUC=97.0&#xa0;% and 96.0&#xa0;%, respectively. The proposed AI-based decision support system improves the automatic segmentation performance of AUS/FLUS and it has shown better performance than available approaches in the literature with respect to ACC, Jaccard and DICE losses. This system has great potential for clinical use by both radiologists and surgeons as well.