Calcium Homeostasis in Parkinson's Disease: From Pathology to Treatment

Preimplantation genetic testing as a preventive strategy for the transmission of mitochondrial DNA disorders

Mitochondrial diseases are distinct types of metabolic and/or neurologic abnormalities that occur as a consequence of dysfunction in oxidative phosphorylation, affecting several systems in the body. There is no effective treatment modality for mitochondrial disorders so far, emphasizing the clinical significance of preventing the inheritance of these disorders. Various reproductive options are available to reduce the probability of inheriting mitochondrial disorders, including in vitro fertilization (IVF) using donated oocytes, preimplantation genetic testing (PGT), and prenatal diagnosis (PND), among which PGT not only makes it possible for families to have genetically-owned children but also PGT has the advantage that couples do not have to decide to terminate the pregnancy if a mutation is detected in the fetus. PGT for mitochondrial diseases originating from nuclear DNA includes analyzing the nuclear genome for the presence or absence of corresponding mutations. However, PGT for mitochondrial disorders arising from mutations in mitochondrial DNA (mtDNA) is more intricate, due to the specific characteristics of mtDNA such as multicopy nature, heteroplasmy phenomenon, and exclusive maternal inheritance. Therefore, the present review aims to discuss the utility and challenges of PGT as a preventive approach to inherited mitochondrial diseases caused by mtDNA mutations.

STIM Proteins: The Gas and Brake of Calcium Entry in Neurons

Stromal interaction molecules (STIM)s are Ca2+ sensors in internal Ca2+ stores of the endoplasmic reticulum. They activate the store-operated Ca2+ channels, which are the main source of Ca2+ entry in non-excitable cells. Moreover, STIM proteins interact with other Ca2+ channel subunits and active transporters, making STIMs an important intermediate molecule in orchestrating a wide variety of Ca2+ influxes into excitable cells. Nevertheless, little is known about the role of STIM proteins in brain functioning. Being involved in many signaling pathways, STIMs replenish internal Ca2+ stores in neurons and mediate synaptic transmission and neuronal excitability. Ca2+ dyshomeostasis is a signature of many pathological conditions of the brain, including neurodegenerative diseases, injuries, stroke, and epilepsy. STIMs play a role in these disturbances not only by supporting abnormal store-operated Ca2+ entry but also by regulating Ca2+ influx through other channels. Here, we review the present knowledge of STIMs in neurons and their involvement in brain pathology.

Designing and Synthesis of Novel Fexofenadine-Derived Hydrazone-Schiff Bases as Potential Urease Inhibitors: In-Vitro, Molecular Docking and DFT Investigations

Thirteen novel hydrazone-Schiff bases (3-15) of fexofenadine were succesfully synthesized, structurally deduced and finally assessed their capability to inhibit urease enzyme (in vitro). In the series, six compounds 12 (IC50=10.19±0.16 μM), 11 (IC50=15.05±1.11 μM), 10 (IC50=17.01±1.23 μM), 9 (IC50=17.22±0.81 μM), 13 (IC50=19.31±0.18 μM), and 14 (IC50=19.62±0.21 μM) displayed strong inhibitory action better than the standard thiourea (IC50=21.14±0.24 μM), while the remaining compounds displayed significant to less inhibition. LUMO and HOMO showed the transferring of charges from molecules to biological transfer and MEP map showed the chemically reactive zone appropriate for drug action are calculated using DFT. AIM charges, non-bonding orbitals, and ELF are also computed. The urease protein binding analysis benefited from the docking studies.

Artificial intelligence in Parkinson's disease: Early detection and diagnostic advancements

Parkinson's disease (PD) is the second most common neurodegenerative disorder, globally affecting men and women at an exponentially growing rate, with currently no cure. Disease progression starts when dopaminergic neurons begin to die. In PD, the loss of neurotransmitter, dopamine is responsible for the overall communication of neural cells throughout the body. Clinical symptoms of PD are slowness of movement, involuntary muscular contractions, speech & writing changes, lessened automatic movement, and chronic tremors in the body. PD occurs in both familial and sporadic forms and modifiable and non-modifiable risk factors and socioeconomic conditions cause PD. Early detectable diagnostics and treatments have been developed in the last several decades. However, we still do not have precise early detectable biomarkers and therapeutic agents/drugs that prevent and/or delay the disease process. Recently, artificial intelligence (AI) science and machine learning tools have been promising in identifying early detectable markers with a greater rate of accuracy compared to past forms of treatment and diagnostic processes. Artificial intelligence refers to the intelligence exhibited by machines or software, distinct from the intelligence observed in humans that is based on neural networks in a form and can be used to diagnose the longevity and disease severity of disease. The term Machine Learning or Neural Networks is a blanket term used to identify an emerging technology that is created to work in the way of a "human brain" using many intertwined neurons to achieve the same level of raw intelligence as that of a brain. These processes have been used for neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease, to assess the severity of the patient's condition. In the current article, we discuss the prevalence and incidence of PD, and currently available diagnostic biomarkers and therapeutic strategies. We also highlighted currently available artificial intelligence science and machine learning tools and their applications to detect disease and develop therapeutic interventions.

Unveiling innovative therapeutic strategies and future trajectories on stimuli-responsive drug delivery systems for targeted treatment of breast carcinoma

This comprehensive review delineates the latest advancements in stimuli-responsive drug delivery systems engineered for the targeted treatment of breast carcinoma. The manuscript commences by introducing mammary carcinoma and the current therapeutic methodologies, underscoring the urgency for innovative therapeutic strategies. Subsequently, it elucidates the logic behind the employment of stimuli-responsive drug delivery systems, which promise targeted drug administration and the minimization of adverse reactions. The review proffers an in-depth analysis of diverse types of stimuli-responsive systems, including thermoresponsive, pH-responsive, and enzyme-responsive nanocarriers. The paramount importance of material choice, biocompatibility, and drug loading strategies in the design of these systems is accentuated. The review explores characterization methodologies for stimuli-responsive nanocarriers and probes preclinical evaluations of their efficacy, toxicity, pharmacokinetics, and biodistribution in mammary carcinoma models. Clinical applications of stimuli-responsive systems, ongoing clinical trials, the potential of combination therapies, and the utility of multifunctional nanocarriers for the co-delivery of assorted drugs and therapies are also discussed. The manuscript addresses the persistent challenge of drug resistance in mammary carcinoma and the potential of stimuli-responsive systems in surmounting it. Regulatory and safety considerations, including FDA guidelines and biocompatibility assessments, are outlined. The review concludes by spotlighting future trajectories and emergent technologies in stimuli-responsive drug delivery, focusing on pioneering approaches, advancements in nanotechnology, and personalized medicine considerations. This review aims to serve as a valuable compendium for researchers and clinicians interested in the development of efficacious and safe stimuli-responsive drug delivery systems for the treatment of breast carcinoma.

Polymeric nanoparticles delivery circumvents bacterial resistance to ciprofloxacin

OBJECTIVE

The efficient inhibition of bacteria and their by-products from infected root canals is hampered by the limitations of traditional root canal disinfection strategies, bacterial resistance to antibiotic drugs, and regenerative endodontics. Polymeric nanoparticles nanocarrier for controlling antibiotic drug delivery were used to overcome the limitations encountered in endodontics treatment.

BACKGROUND

Several polymeric nanoparticles have been used for the delivery of ciprofloxacin drug. The application of poly (ethylene glycol) methyl ether-block-poly(lactide-co-glycolide) (PEG-PLGA) nanoparticles has highlighted the clean and safe delivery of ciprofloxacin (CIP) hydrophilic drug for endodontics treatment. PEG/PLGA was prepared using the solid/oil/water method and the CIP was loaded into polymeric nanoparticles via an ion pairing agent.

RESULTS

The CIP-loaded PEG-PLGA nanoparticles have a spherical shape with a 120 ± 0.43 nm size, the CIP encapsulating efficiency was 63.26 ± 9.24% with a loading content of 7.75 ± 1.13%, and sustained release was achieved over 168 h which followed Higuchi model with a non-Fickian mechanism. Moreover, CIP-loaded PEG-PLGA had low cytotoxicity to the stem cells of the apical papilla.

CONCLUSION

The results conclude invigorating future perspectives of polymeric nanoparticles for a wide range of drug delivery for various disease treatments. It's anticipated that these polymeric nanoparticles may divert new expectations in the future for topical antibiotic drug delivery with discrete intracellular medicament, and a safe and clean environment.

Insights into Calpain Activation and Rho-ROCK Signaling in Parkinson's Disease and Aging

Parkinson's disease (PD), a progressive neurodegenerative disease, has no cure, and current therapies are not effective at halting disease progression. The disease affects mid-brain dopaminergic neurons and, subsequently, the spinal cord, contributing to many debilitating symptoms associated with PD. The GTP-binding protein, Rho, plays a significant role in the cellular pathology of PD. The downstream effector of Rho, Rho-associated kinase (ROCK), plays multiple functions, including microglial activation and induction of inflammatory responses. Activated microglia have been implicated in the pathology of many neurodegenerative diseases, including PD, that initiate inflammatory responses, leading to neuron death. Calpain expression and activity is increased following glial activation, which triggers the Rho-ROCK pathway and induces inflammatory T cell activation and migration as well as mediates toxic α-synuclein (α-syn) aggregation and neuron death, indicating a pivotal role for calpain in the inflammatory and degenerative processes in PD. Increased calpain activity and Rho-ROCK activation may represent a new mechanism for increased oxidative damage in aging. This review will summarize calpain activation and the role of the Rho-ROCK pathway in oxidative stress and α-syn aggregation, their influence on the neurodegenerative process in PD and aging, and possible strategies and research directions for therapeutic intervention.

Molecular Insights into the Relationship Between Platelet Activation and Endothelial Dysfunction: Molecular Approaches and Clinical Practice

Platelets are one of the coagulation cells. When platelet activation occurs, many mediators are released and affect endothelial cells (ECs) and lead to endothelial dysfunction (ED). ED plays an important role in the pathogenesis of many diseases, including cardiovascular disease (CVD). Platelet are of important factors in ED. The release of mediators by platelets causes the stimulation of inflammatory pathways, oxidative stress, and apoptosis, which ultimately result in ED.On the other hand, platelet activation in CVD patients can be associated with a bad prognosis. Platelet activation can increase the level of markers such as p-selectin in the serum. Also, in this study, we have discussed the role of platelet as a diagnostic factor, as well as its use as a treatment option. In addition, we discussed some of the molecular pathways that are used to target platelet activation.

Increased V-ATPase activity can lead to chemo-resistance in oral squamous cell carcinoma via autophagy induction: new insights

Oral squamous cell carcinoma (OSCC) is a cancer type with a high rate of recurrence and a poor prognosis. Tumor chemo-resistance remains an issue for OSCC patients despite the availability of multimodal therapy options, which causes an increase in tumor invasiveness. Vacuolar ATPase (V-ATPase), appears to be one of the most significant molecules implicated in MDR in tumors like OSCC. It is primarily responsible for controlling the acidity in the solid tumors' microenvironment, which interferes with the absorption of chemotherapeutic medications. However, the exact cellular and molecular mechanisms V-ATPase plays in OSCC chemo-resistance have not been understood. Uncovering these mechanisms can contribute to combating OSCC chemo-resistance and poor prognosis. Hence, in this review, we suggest that one of these underlying mechanisms is autophagy induced by V-ATPase which can potentially contribute to OSCC chemo-resistance. Finally, specialized autophagy and V-ATPase inhibitors may be beneficial as an approach to reduce drug resistance to anticancer therapies in addition to serving as coadjuvants in antitumor treatments. Also, V-ATPase could be a prognostic factor for OSCC patients. However, in the future, more investigations are required to demonstrate these suggestions and hypotheses.

Updated review on analysis of long non-coding RNAs as emerging diagnostic and therapeutic targets in prostate cancers

Despite advancements, prostate cancers (PCa) pose a significant global health challenge due to delayed diagnosis and therapeutic resistance. This review delves into the complex landscape of prostate cancer, with a focus on long-noncoding RNAs (lncRNAs). Also explores the influence of aberrant lncRNAs expression in progressive PCa stages, impacting traits like proliferation, invasion, metastasis and therapeutic resistance. The study elucidates how lncRNAs modulate crucial molecular effectors, including transcription factors and microRNAs, affecting signaling pathways such as androgen receptor signaling. Besides, this manuscript sheds light on novel concepts and mechanisms driving PCa progression through lncRNAs, providing a critical analysis of their impact on the disease's diverse characteristics. Besides, it discusses the potential of lncRNAs as diagnostics and therapeutic targets in PCa. Collectively, this work highlights state of art mechanistic comprehension and rigorous scientific approaches to advance our understanding of PCa and depict innovations in this evolving field of research.

Cardiovascular challenges in the era of antiretroviral therapy for AIDS/ HIV: A comprehensive review of research advancements, pathophysiological insights, and future directions

Cardiovascular disease, particularly coronary heart disease, is becoming more common among those living with HIV. Individuals with HIV face an increased susceptibility to myocardial infarction, also known as a heart attack, as compared to the general population in developed countries. This heightened risk can be attributed mainly to the presence of effective antiretroviral drugs and the resulting longer lifespan. Some cardiac issues linked to non-antiretroviral medications, including myocarditis, endocarditis, cardiomyopathy with dilation, pulmonary hypertension, and oedema of the heart, may affect those not undergoing highly active antiretroviral therapy (ART). Impaired immune function and systemic inflammation are significant contributors to this phenomenon after initiating highly aggressive antiretroviral treatment ART. It is becoming more challenging to determine the best course of treatment for HIV-associated cardiomyopathy due to new research suggesting that protease inhibitors might have a negative impact on the development of HF. Currently, the primary focus of research on ART medications is centered on the cardiovascular adverse effects of nucleoside reverse transcriptase inhibitors and protease inhibitors. This review paper thoroughly evaluates the advancements achieved in cardiovascular disease research and explores the potential implications for prospects. Additionally, it considers the field's future prospects while examining how ART might be altered and its clinical applications.

Synthesis, biochemical and computational evaluations of novel bis-acylhydrazones of 2,2'-(1,1'-biphenyl)-4,4'-diylbis(oxy))di(acetohydrazide) as dual cholinesterase inhibitors

A series of twenty-seven bis(acylhydrazones) were successfully synthesized with high yields through a multistep process, which entailed the esterification of hydroxyl groups, hydrazination with an excess of hydrazine hydrate, and subsequent reactions with various carbonyl moieties (aldehydes). In the final stage of synthesis, different chemical species including aromatic, heterocyclic, and aliphatic compounds were integrated into the framework. The resulting compounds were characterized using several spectroscopic techniques (1H NMR, 13C NMR, and mass spectrometry). Their anticholinesterase activities were assessed in vitro by examining their interactions with two cholinesterase enzymes: acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). Among the synthesized hits, compounds 3, 5, 6, 9-12, and 14 exhibited good to moderate inhibition of AChE. Specifically, 10 (IC50 = 26.3 ± 0.4 μM) and 11 (IC50 = 28.4 ± 0.5 μM) showed good inhibitory activity against AChE, while 9, 12, 3, and 6 exhibited significant inhibition potential against AChE with IC50 values ranging from 35.2 ± 1.1 μM to 64.4 ± 0.3 μM. On the other hand, 5 (IC50 = 22.0 ± 1.1 μM) and 27 (IC50 = 31.3 ± 1.3 μM) displayed significant, and 19 (IC50 = 92.6 ± 0.4 μM) showed moderate inhibitory potential for BChE. Notably, 5 and 27 exhibited dual inhibition of AChE and BChE, with greater potency than the standard drug galantamine. The binding patterns of these molecules within the binding cavities of AChE and BChE were anticipated by molecular docking which showed good correlation with our in vitro findings. Further structural optimization of these molecules may yield more potent AChE and BChE inhibitors.

Beyond the silence: A comprehensive exploration of long non-coding RNAs as genetic whispers and their essential regulatory functions in cardiovascular disorders

Long non-coding RNAs (lncRNAs) are RNA molecules that regulate gene expression at several levels, including transcriptional, post-transcriptional, and translational. They have a length of more than 200 nucleotides and cannot code. Many human diseases have been linked to aberrant lncRNA expression, highlighting the need for a better knowledge of disease etiology to drive improvements in diagnostic, prognostic, and therapeutic methods. Cardiovascular diseases (CVDs) are one of the leading causes of death worldwide. LncRNAs play an essential role in the complex process of heart formation, and their abnormalities have been associated with several CVDs. This Review article looks at the roles and relationships of long non-coding RNAs (lncRNAs) in a wide range of CVDs, such as heart failure, myocardial infarction, atherosclerosis, and cardiac hypertrophy. In addition, the review delves into the possible uses of lncRNAs in diagnostics, prognosis, and clinical treatments of cardiovascular diseases. Additionally, it considers the field's future prospects while examining how lncRNAs might be altered and its clinical applications.

Machine learning algorithms for FPGA Implementation in biomedical engineering applications: A review

Field Programmable Gate Arrays (FPGAs) are integrated circuits that can be configured by the user after manufacturing, making them suitable for customized hardware prototypes, a feature not available in general-purpose processors in Application Specific Integrated Circuits (ASIC). In this paper, we review the vast Machine Learning (ML) algorithms implemented on FPGAs to increase performance and capabilities in healthcare technology over 2001-2023. In particular, we focus on real-time ML algorithms targeted to FPGAs and hybrid System-on-a-chip (SoC) FPGA architectures for biomedical applications. We discuss how previous works have customized and optimized their ML algorithm and FPGA designs to address the putative embedded systems challenges of limited memory, hardware, and power resources while maintaining scalability to accommodate different network sizes and topologies. We provide a synthesis of articles implementing classifiers and regression algorithms, as they are significant algorithms that cover a wide range of ML algorithms used for biomedical applications. This article is written to inform the biomedical engineering and FPGA design communities to advance knowledge of FPGA-enabled ML accelerators for biomedical applications.

Integrating hybrid transfer learning with attention-enhanced deep learning models to improve breast cancer diagnosis

Cancer, with its high fatality rate, instills fear in countless individuals worldwide. However, effective diagnosis and treatment can often lead to a successful cure. Computer-assisted diagnostics, especially in the context of deep learning, have become prominent methods for primary screening of various diseases, including cancer. Deep learning, an artificial intelligence technique that enables computers to reason like humans, has recently gained significant attention. This study focuses on training a deep neural network to predict breast cancer. With the advancements in medical imaging technologies such as X-ray, magnetic resonance imaging (MRI), and computed tomography (CT) scans, deep learning has become essential in analyzing and managing extensive image datasets. The objective of this research is to propose a deep-learning model for the identification and categorization of breast tumors. The system's performance was evaluated using the breast cancer identification (BreakHis) classification datasets from the Kaggle repository and the Wisconsin Breast Cancer Dataset (WBC) from the UCI repository. The study's findings demonstrated an impressive accuracy rate of 100%, surpassing other state-of-the-art approaches. The suggested model was thoroughly evaluated using F1-score, recall, precision, and accuracy metrics on the WBC dataset. Training, validation, and testing were conducted using pre-processed datasets, leading to remarkable results of 99.8% recall rate, 99.06% F1-score, and 100% accuracy rate on the BreakHis dataset. Similarly, on the WBC dataset, the model achieved a 99% accuracy rate, a 98.7% recall rate, and a 99.03% F1-score. These outcomes highlight the potential of deep learning models in accurately diagnosing breast cancer. Based on our research, it is evident that the proposed system outperforms existing approaches in this field.

Novel hydrazone schiff's base derivatives of polyhydroquinoline: synthesis, in vitro prolyl oligopeptidase inhibitory activity and their Molecular docking study

This research work reports the synthesis of new derivatives of the hydrazone Schiff bases (1-17) based on polyhydroquinoline nucleus through multistep reactions. HR-ESIMS,1H- and 13C-NMR spectroscopy were used to structurally infer all of the synthesized compounds and lastly evaluated for prolyl oligopeptidase inhibitory activity. All the prepared products displayed good to excellent inhibitory activity when compared with standard z-prolyl-prolinal. Three derivatives 3, 15 and 14 showed excellent inhibition with IC50 values 3.21 ± 0.15 to 5.67 ± 0.18 µM, while the remaining 12 compounds showed significant activity. Docking studies indicated a good correlation with the biochemical potency of compounds estimated in the in-vitro test and showed the potency of compounds 3, 15 and 14. The MD simulation results confirmed the stability of the most potent inhibitors 3, 15 and 14 at 250 ns using the parameters RMSD, RMSF, Rg and number of hydrogen bonds. The RMSD values indicate the stability of the protein backbone in complex with the inhibitors over the simulation time. The RMSF values of the binding site residues indicate that the potent inhibitors contributed to stabilizing these regions of the protein, through formed stable interactions with the protein. The Rg. analysis assesses the overall size and compactness of the complexes. The maintenance of stable hydrogen bonds suggests the existence of favorable binding interactions. SASA analysis suggests that they maintained stable conformations without large-scale exposure to the solvent. These results indicate that the ligand-protein interactions are stable and could be exploited to design new drugs for disease treatment.Communicated by Ramaswamy H. Sarma.

Synthesis, molecular docking and DFT analysis of novel bis-Schiff base derivatives with thiobarbituric acid for α-glucosidase inhibition assessment

A library of novel bis-Schiff base derivatives based on thiobarbituric acid has been effectively synthesized by multi-step reactions as part of our ongoing pursuit of novel anti-diabetic agents. All these derivatives were subjected to in vitro α-glucosidase inhibitory potential testing after structural confirmation by modern spectroscopic techniques. Among them, compound 8 (IC50 = 0.10 ± 0.05 µM), and 9 (IC50 = 0.13 ± 0.03 µM) exhibited promising inhibitory activity better than the standard drug acarbose (IC50 = 0.27 ± 0.04 µM). Similarly, derivatives (5, 6, 7, 10 and 4) showed significant to good inhibitory activity in the range of IC50 values from 0.32 ± 0.03 to 0.52 ± 0.02 µM. These derivatives were docked with the target protein to elucidate their binding affinities and key interactions, providing additional insights into their inhibitory mechanisms. The chemical nature of these compounds were reveal by performing the density functional theory (DFT) calculation using hybrid B3LYP functional with 6-311++G(d,p) basis set. The presence of intramolecular H-bonding was explored by DFT-d3 and reduced density gradient (RGD) analysis. Furthermore, various reactivity parameters were explored by performing TD-DFT at CAM-B3LYP/6-311++G(d,p) method.

Ferroptosis-related exosomal non-coding RNAs: promising targets in pathogenesis and treatment of non-malignant diseases

Ferroptosis, an iron-dependent form of programmed cell death, introduces a novel perspective on cellular demise. This study investigates the regulatory network of exosomal non-coding RNAs (ncRNAs), including miRNAs, circRNAs, and lncRNAs, in ferroptosis modulation. The primary goal is to examine the pathological roles of ferroptosis-related exosomal ncRNAs, particularly in ischemic reperfusion injuries. The research reveals intricate molecular interactions governing the regulatory interplay between exosomal ncRNAs and ferroptosis, elucidating their diverse roles in different non-malignant pathological contexts. Attention is given to their impact on diseases, including cardiac, cerebral, liver, and kidney ischemic injuries, as well as lung, wound, and neuronal injuries. Beyond theoretical exploration, the study provides insights into potential therapeutic applications, emphasizing the significance of mesenchymal stem cells (MSCs)-derived exosomes. Findings underscore the pivotal role of MSC-derived exosomal ncRNAs in modulating cellular responses related to ferroptosis regulation, introducing a cutting-edge dimension. This recognition emphasizes the importance of MSC-derived exosomes as crucial mediators with broad therapeutic implications. Insights unveil promising avenues for targeted interventions, capitalizing on the diverse roles of exosomal ncRNAs, providing a comprehensive foundation for future therapeutic strategies.

The progressive trend of modeling and drug screening systems of breast cancer bone metastasis

Bone metastasis is considered as a considerable challenge for breast cancer patients. Various in vitro and in vivo models have been developed to examine this occurrence. In vitro models are employed to simulate the intricate tumor microenvironment, investigate the interplay between cells and their adjacent microenvironment, and evaluate the effectiveness of therapeutic interventions for tumors. The endeavor to replicate the latency period of bone metastasis in animal models has presented a challenge, primarily due to the necessity of primary tumor removal and the presence of multiple potential metastatic sites.The utilization of novel bone metastasis models, including three-dimensional (3D) models, has been proposed as a promising approach to overcome the constraints associated with conventional 2D and animal models. However, existing 3D models are limited by various factors, such as irregular cellular proliferation, autofluorescence, and changes in genetic and epigenetic expression. The imperative for the advancement of future applications of 3D models lies in their standardization and automation. The utilization of artificial intelligence exhibits the capability to predict cellular behavior through the examination of substrate materials' chemical composition, geometry, and mechanical performance. The implementation of these algorithms possesses the capability to predict the progression and proliferation of cancer. This paper reviewed the mechanisms of bone metastasis following primary breast cancer. Current models of breast cancer bone metastasis, along with their challenges, as well as the future perspectives of using these models for translational drug development, were discussed.

Beyond the beat: A pioneering investigation into exercise modalities for alleviating diabetic cardiomyopathy and enhancing cardiac health

Patients with preexisting cardiovascular disease or those at high risk for developing the condition are often offered exercise as a form of therapy. Patients with cancer who are at an increased risk for cardiovascular issues are increasingly encouraged to participate in exercise-based, interdisciplinary programs due to the positive correlation between these interventions and clinical outcomes following myocardial infarction. Diabetic cardiomyopathy (DC) is a cardiac disorder that arises due to disruptions in the homeostasis of individuals with diabetes. One of the primary reasons for mortality in individuals with diabetes is the presence of cardiac structural damage and functional abnormalities, which are the primary pathological features of DC. The aetiology of dilated cardiomyopathy is multifaceted and encompasses a range of processes, including metabolic abnormalities, impaired mitochondrial function, dysregulation of calcium ion homeostasis, excessive cardiomyocyte death, and fibrosis. In recent years, many empirical investigations have demonstrated that exercise training substantially impacts the prevention and management of diabetes. Exercise has been found to positively impact the recovery of diabetes and improve several metabolic problem characteristics associated with DC. One potential benefit of exercise is its ability to increase systolic activity, which can enhance cardiometabolic and facilitate the repair of structural damage to the heart caused by DC, leading to a direct improvement in cardiac health. In contrast, exercise has the potential to indirectly mitigate the pathological progression of DC through its ability to decrease circulating levels of sugar and fat while concurrently enhancing insulin sensitivity. A more comprehensive understanding of the molecular mechanism via exercise facilitates the restoration of DC disease must be understood. Our goal in this review was to provide helpful information and clues for developing new therapeutic techniques for motion alleviation DC by examining the molecular mechanisms involved.

Discovering genetic mechanisms underlying the co-occurrence of Parkinson's disease and non-motor traits

Understanding the biological mechanisms that underlie the non-motor symptoms of Parkinson's disease (PD) requires comprehensive frameworks that unravel the complex interplay of genetic risk factors. Here, we used a disease-agnostic brain cortex gene regulatory network integrated with Mendelian Randomization analyses that identified 19 genes whose changes in expression were causally linked to PD. We further used the network to identify genes that are regulated by PD-associated genome-wide association study (GWAS) SNPs. Extended protein interaction networks derived from PD-risk genes and PD-associated SNPs identified convergent impacts on biological pathways and phenotypes, connecting PD with established co-occurring traits, including non-motor symptoms. These findings hold promise for therapeutic development. In conclusion, while distinct sets of genes likely influence PD risk and outcomes, the existence of genes in common and intersecting pathways associated with other traits suggests that they may contribute to both increased PD risk and symptom heterogeneity observed in people with Parkinson's.

In silico Approaches for Exploring the Pharmacological Activities of Benzimidazole Derivatives: A Comprehensive Review

BACKGROUND

This article reviews computational research on benzimidazole derivatives. Cytotoxicity for all compounds against cancer cell lines was measured and the results revealed that many compounds exhibited high inhibitions. This research examines the varied pharmacological properties like anticancer, antibacterial, antioxidant, anti-inflammatory and anticonvulsant activities of benzimidazole derivatives. The suggested method summarises In silico research for each activity. This review examines benzimidazole derivative structure-activity relationships and pharmacological effects. In silico investigations can anticipate structural alterations and their effects on these derivative's pharmacological characteristics and efficacy through many computational methods. Molecular docking, molecular dynamics simulations and virtual screening help anticipate pharmacological effects and optimize chemical design. These trials will improve lead optimization, target selection, and ADMET property prediction in drug development. In silico benzimidazole derivative studies will be assessed for gaps and future research. Prospective studies might include empirical verification, pharmacodynamic analysis, and computational methodology improvement.

OBJECTIVES

This review discusses benzimidazole derivative In silico research to understand their specific pharmacological effects. This will help scientists design new drugs and guide future research.

METHODS

Latest, authentic and published reports on various benzimidazole derivatives and their activities are being thoroughly studied and analyzed.

RESULT

The overview of benzimidazole derivatives is more comprehensive, highlighting their structural diversity, synthetic strategies, mechanisms of action, and the computational tools used to study them.

CONCLUSION

In silico studies help to understand the structure-activity relationship (SAR) of benzimidazole derivatives. Through meticulous alterations of substituents, ring modifications, and linker groups, this study identified the structural factors influencing the pharmacological activity of benzimidazole derivatives. These findings enable the rational design and optimization of more potent and selective compounds.

Deeper insight into ferroptosis: association with Alzheimer's, Parkinson's disease, and brain tumors and their possible treatment by nanomaterials induced ferroptosis

Ferroptosis is an emerging and novel type of iron-dependent programmed cell death which is mainly caused by the excessive deposition of free intracellular iron in the brain cells. This deposited free iron exerts a ferroptosis pathway, resulting in lipid peroxidation (LiPr). There are mainly three ferroptosis pathways viz. iron metabolism-mediated cysteine/glutamate, and LiPr-mediated. Iron is required by the brain as a redox metal for several physiological activities. Due to the iron homeostasis balance disruption, the brain gets adversely affected which further causes neurodegenerative diseases (NDDs) like Parkinson's and Alzheimer's disease, strokes, and brain tumors like glioblastoma (GBS), and glioma. Nanotechnology has played an important role in the prevention and treatment of these NDDs. A synergistic effect of nanomaterials and ferroptosis could prove to be an effective and efficient approach in the field of nanomedicine. In the current review, the authors have highlighted all the latest research in the field of ferroptosis, specifically emphasizing on the role of major molecular key players and various mechanisms involved in the ferroptosis pathway. Moreover, here the authors have also addressed the correlation of ferroptosis with the pathophysiology of NDDs and theragnostic effect of ferroptosis and nanomaterials for the prevention and treatment of NDDs.

TGRA-P: Task-driven model predicts 90-day mortality from ICU clinical notes on mechanical ventilation

BACKGROUND

With the outbreak and spread of COVID-19 worldwide, limited ventilators fail to meet the surging demand for mechanical ventilation in the ICU. Clinical models based on structured data that have been proposed to rationalize ventilator allocation often suffer from poor ductility due to fixed fields and laborious normalization processes. The advent of pre-trained models and downstream fine-tuning methods allows for learning large amounts of unstructured clinical text for different tasks. But the hardware requirements of large-scale pre-trained models and purposeless networks downstream have led to a lack of promotion in the clinical domain.

OBJECTIVE

In this study, an innovative architecture of a task-driven predictive model is proposed and a Task-driven Gated Recurrent Attention Pool model (TGRA-P) is developed based on the architecture. TGRA-P predicts early mortality risk from patients' clinical notes on mechanical ventilation in the ICU, which is used to assist clinicians in diagnosis and decision-making.

METHODS

Specifically, a Task-Specific Embedding Module is proposed to fine-tune the embedding with task labels and save it as static files for downstream calls. It serves the task better and prevents GPU overload. The Gated Recurrent Attention Unit (GRA) is proposed to further enhance the dependency of the information preceding and following the text sequence with fewer parameters. In addition, we propose a Residual Max Pool (RMP) to avoid ignoring words in common text classification tasks by incorporating all word-level features of the notes for prediction. Finally, we use a fully connected decoding network as a classifier to predict the mortality risk.

RESULT

The proposed model shows very promising results with an AUROC of 0.8245±0.0096, an AUPRC of 0.7532±0.0115, an accuracy of 0.7422±0.0028 and F1-score of 0.6612±0.0059 for 90-day mortality prediction using clinical notes of ICU mechanically ventilated patients on the MIMIC-III dataset, all of which are better than previous studies. Moreover, the superiority of the proposed model in comparison with other baseline models is also statistically validated through the calculated Cohen's d effect sizes.

CONCLUSION

The experimental results show that TGRA-P based on the innovative task-driven prognostic architecture obtains state-of-the-art performance. In future work, we will build upon the provided code and investigate its applicability to different datasets. The model balances performance and efficiency, not only reducing the cost of early mortality risk prediction but also assisting physicians in making timely clinical interventions and decisions. By incorporating textual records that are challenging for clinicians to utilize, the model serves as a valuable complement to physicians' judgment, enhancing their decision-making process.

Medical image analysis using deep learning algorithms

In the field of medical image analysis within deep learning (DL), the importance of employing advanced DL techniques cannot be overstated. DL has achieved impressive results in various areas, making it particularly noteworthy for medical image analysis in healthcare. The integration of DL with medical image analysis enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This extensive review of existing literature conducts a thorough examination of the most recent deep learning (DL) approaches designed to address the difficulties faced in medical healthcare, particularly focusing on the use of deep learning algorithms in medical image analysis. Falling all the investigated papers into five different categories in terms of their techniques, we have assessed them according to some critical parameters. Through a systematic categorization of state-of-the-art DL techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Long Short-term Memory (LSTM) models, and hybrid models, this study explores their underlying principles, advantages, limitations, methodologies, simulation environments, and datasets. Based on our results, Python was the most frequent programming language used for implementing the proposed methods in the investigated papers. Notably, the majority of the scrutinized papers were published in 2021, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical image analysis, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image analysis within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of image analysis in medical healthcare research. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, sensitivity, specificity, F-score, robustness, computational complexity, and generalizability.

A review on immunoglobulin Y (IgY) conjugated with metal nanoparticles and biomedical uses

Today, the use of nanoparticles has attracted considerable attention in biomedical investigations and applications. Antibody-nanoparticle conjugates have proven to be useful tools for raising accuracy and sensitivity in in vitro diagnostics. IgY antibodies have benefits over different antibodies in terms of minimizing animal harm, reducing reactivity with mammalian factors, and cost-effective extraction. Metal nanoparticles are widely used for various medical and biological applications and are potential candidates for identifying pathogens and treating them, which can be mostly related to their special properties, including their shape and size. Avian IgY antibodies conjugated with nanoparticles have been widely used for the detection of parasitic, viral, and bacterial infections as well as allergens and toxicological and pharmaceutical molecules. This review aimed to investigate avian antibodies conjugated with metal nanoparticles and their biological applications.

Evaluation of common protein biomarkers involved in the pathogenesis of respiratory diseases with proteomic methods: A systematic review

AIM

Respiratory disease (RD) is one of the most common diseases characterized by lung dysfunction. Many diagnostic mechanisms have been used to identify the pathogenic agents of responsible for RD. Among these, proteomics emerges as a valuable diagnostic method for pinpointing the specific proteins involved in RD pathogenesis. Therefore, in this study, for the first time, we examined the protein markers involved in the pathogenesis of chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), asthma, bronchiolitis obliterans (BO), and chemical warfare victims exposed to mustard gas, using the proteomics method as a systematic study.

MATERIALS AND METHODS

A systematic search was performed up to September 2023 on several databases, including PubMed, Scopus, ISI Web of Science, and Cochrane. In total, selected 4246 articles were for evaluation according to the criteria. Finally, 119 studies were selected for this systematic review.

RESULTS

A total of 13,806 proteins were identified, 6471 in COPD, 1603 in Asthma, 5638 in IPF, three in BO, and 91 in mustard gas exposed victims. Alterations in the expression of these proteins were observed in the respective diseases. After evaluation, the results showed that 31 proteins were found to be shared among all five diseases.

CONCLUSION

Although these 31 proteins regulate different factors and molecular pathways in all five diseases, they ultimately lead to the regulation of inflammatory pathways. In other words, the expression of some proteins in COPD and mustard-exposed patients increases inflammatory reactions, while in IPF, they cause lung fibrosis. Asthma, causes allergic reactions due to T-cell differentiation toward Th2.

Drug permeation enhancement, efficacy, and safety assessment of azelaic acid loaded SNEDDS hydrogel to overcome the treatment barriers of atopic dermatitis

Atopic dermatitis is one of the most widespread chronic inflammatory skin conditions that can occur at any age, though the prevalence is highest in children. The purpose of the current study was to prepare and optimize the azelaic acid (AzA) loaded SNEDDS using Pseudo ternary phase diagram, which was subsequently incorporated into the Carbopol 940 hydrogel for the treatment of atopic dermatitis. The composition was evaluated for size, entrapment efficiency, in vitro, ex vivo, and in vivo studies. The polydispersity index of the optimized preparation was found to be less than 0.5, and the size of the distributed globules was found to be 151.20 ± 3.67 nm. The SNEDDS hydrogel was characterized for pH, viscosity, spreadability, and texture analysis. When compared to the marketed formulation, SNEDDS hydrogel was found to have a higher rate of permeation through the rat skin. In addition, a skin irritation test carried out on experimental animals showed that the SNEDDS formulation did not exhibit any erythematous symptoms after a 24-h exposure. In conclusion, the topical delivery of AzA through the skin using SNEDDS hydrogel could prove to be an effective approach for the treatment of atopic dermatitis.

The association between neutrophil-to-lymphocyte ratio and disease activity in rheumatoid arthritis

The inflammatory response is responsible for the promotion of pannus development over the joint, which is the primary factor in joint injury in rheumatoid arthritis (RA). More in-depth investigations have been conducted in recent years leading to a greater understanding of RA. Yet, it's difficult to gauge inflammation levels in RA patients. Some people who have RA do not exhibit normal symptoms, which makes it more challenging to make a diagnosis. Typical RA evaluations are subject to a few restrictions. Earlier research demonstrated that some patients continued to experience the progression of bone and joint degeneration even while in clinical remission. This progression was attributed to ongoing synovial inflammation. As a result, performing a precise evaluation of the level of inflammation is of the utmost importance. The neutrophil-to-lymphocyte ratio (NLR) has consistently been one of the most interesting novel non-specific inflammatory indicators. It is a reflection of the equilibrium between lymphocytes and neutrophils, which are inflammatory regulators and inflammatory activators, respectively. A higher NLR is linked to more severe levels of imbalance and inflammation. The aim of this study was to depict the role of NLR in RA progression and to show if NLR could predict the response to disease-modifying antirheumatic drugs (DMARDs) therapy in RA.

Adult hippocampal neurogenesis (AHN) controls central nervous system and promotes peripheral nervous system regeneration via physical exercise

Physical exercise has beneficial effects on adult hippocampal neurogenesis (AHN) and cognitive processes, including learning. Although it is not known if anaerobic resistance training and high-intensity interval training, which involve alternating brief bouts of highly intense anaerobic activity with rest periods, have comparable effects on AHN. Also, while less thoroughly investigated, individual genetic diversity in the overall response to physical activity is likely to play a key role in the effects of exercise on AHN. Physical exercise has been shown to improve health on average, although the benefits may vary from person to person, perhaps due to genetic differences. Maximal aerobic capacity and metabolic health may improve significantly with aerobic exercise for some people, while the same amount of training may have little effect on others. This review discusses the AHN's capability for peripheral nervous system (PNS) regeneration and central nervous system (CNS) control via physical exercise. Exercise neurogenicity, effective genes, growth factors, and the neurotrophic factors involved in PNS regeneration and CNS control were discussed. Also, some disorders that could be affected by AHN and physical exercise are summarized.

Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models

One of the most promising research initiatives in the healthcare field is focused on the rising incidence of skin cancer worldwide and improving early discovery methods for the disease. The most significant factor in the fatalities caused by skin cancer is the late identification of the disease. The likelihood of human survival may be significantly improved by performing an early diagnosis followed by appropriate therapy. It is not a simple process to extract the elements from the photographs of the tumors that may be used for the prospective identification of skin cancer. Several deep learning models are widely used to extract efficient features for a skin cancer diagnosis; nevertheless, the literature demonstrates that there is still room for additional improvements in various performance metrics. This study proposes a hybrid deep convolutional neural network architecture for identifying skin cancer by adding two main heuristics. These include Xception and MobileNetV2 models. Data augmentation was introduced to balance the dataset, and the transfer learning technique was utilized to resolve the challenges of the absence of labeled datasets. It has been detected that the suggested method of employing Xception in conjunction with MobileNetV2 attains the most excellent performance, particularly concerning the dataset that was evaluated: specifically, it produced 97.56% accuracy, 97.00% area under the curve, 100% sensitivity, 93.33% precision, 96.55% F1 score, and 0.0370 false favorable rates. This research has implications for clinical practice and public health, offering a valuable tool for dermatologists and healthcare professionals in their fight against skin cancer.