Revolutionizing cancer care strategies: immunotherapy, gene therapy, and molecular targeted therapy.

Despite the availability of technological advances in traditional anti-cancer therapies, there is a need for more precise and targeted cancer treatment strategies. The wide-ranging shortfalls of conventional anticancer therapies such as systematic toxicity, compromised life quality, and limited to severe side effects are major areas of concern of conventional cancer treatment approaches. Owing to the expansion of knowledge and technological advancements in the field of cancer biology, more innovative and safe anti-cancerous approaches such as immune therapy, gene therapy and targeted therapy are rapidly evolving with the aim to address the limitations of conventional therapies. The concept of immunotherapy began with the capability of coley toxins to stimulate toll-like receptors of immune cells to provoke an immune response against cancers. With an in-depth understating of the molecular mechanisms of carcinogenesis and their relationship to disease prognosis, molecular targeted therapy approaches, that inhibit or stimulate specific cancer-promoting or cancer-inhibitory molecules respectively, have offered promising outcomes. In this review, we evaluate the achievement and challenges of these technically advanced therapies with the aim of presenting the overall progress and perspective of each approach.

MDM2- an indispensable player in tumorigenesis.

Murine double minute 2 (MDM2) is a well-recognized molecule for its oncogenic potential. Since its identification, various cancer-promoting roles of MDM2 such as growth stimulation, sustained angiogenesis, metabolic reprogramming, apoptosis evasion, metastasis, and immunosuppression have been established. Alterations in the expression levels of MDM2 occur in multiple types of cancers resulting in uncontrolled proliferation. The cellular processes are modulated by MDM2 through transcription, post-translational modifications, protein degradation, binding to cofactors, and subcellular localization. In this review, we discuss the precise role of deregulated MDM2 levels in modulating cellular functions to promote cancer growth. Moreover, we also briefly discuss the role of MDM2 in inducing resistance against anti-cancerous therapies thus limiting the benefits of cancerous treatment.

Holistic expression of miR-17-92 cluster in obesity, kidney diseases, cardiovascular diseases, and diabetes.

miR-17-92 cluster encodes six micro RNAs (miRNAs) and plays a crucial role in the regulation of various cellular processes. Aberrant expression of this cluster may result in the onset of several diseases. Initially, the role of miR-17-92 cluster in tumorigenesis was discovered but recent research has also uncovered its role in other diseases. Members of the cluster may serve as potential biomarkers in the prognosis, diagnosis, and treatment of several diseases and their complications. In this article, we have reviewed the recent research carried out on the expression pattern of miR-17-92 cluster in non-communicable diseases i.e., obesity, cardiovascular diseases (CVD), kidney diseases (KD) and diabetes mellitus (DM). We examined miR-17-92 role in pathological processes and their potential importance as biomarkers. Each member of the cluster miR-17-92 was upregulated in obesity. miR-18a, miR-19b-3p, miR20a, and miR92a were significantly upregulated in CVD. An equal fraction of the cluster was dysregulated (upregulated and downregulated) in diabetes; however, miR-17-92 was downregulated in most studies on CKD.

High level of lactate dehydrogenase and ischaemia-reperfusion injury regulate the multiple organ dysfunction in patients with COVID-19.

BACKGROUND: Multiple organ damage has been observed in patients with COVID-19, but the exact pathway is not known. Vital organs of the human body may get affected after replication of SARS-CoV-2, including the lungs, heart, kidneys, liver and brain. It triggers severe inflammation and impairs the function of two or more organ systems. Ischaemia-reperfusion (IR) injury is a phenomenon that can have disastrous effects on the human body. METHODS: In this study, we analysed the laboratory data of 7052 hospitalised patients with COVID-19 including lactate dehydrogenase (LDH). A total of 66.4% patients were men and 33.6% were women, which indicated gender difference as a prominent factor to be considered. RESULTS: Our data showed high levels of inflammation and elevated markers of tissue injury from multiple organs C reactive protein, white blood cell count, alanine transaminase, aspartate aminotransferase and LDH. The number of red blood cells, haemoglobin concentration and haematocrit were lower than normal which indicated a reduction in oxygen supply and anaemia. CONCLUSION: On the basis of these results, we proposed a model linking IR injury to multiple organ damage by SARS-CoV-2. COVID-19 may cause a reduction in oxygen towards an organ, which leads to IR injury.

Road Feature Detection for Advance Driver Assistance System Using Deep Learning.

Hundreds of people are injured or killed in road accidents. These accidents are caused by several intrinsic and extrinsic factors, including the attentiveness of the driver towards the road and its associated features. These features include approaching vehicles, pedestrians, and static fixtures, such as road lanes and traffic signs. If a driver is made aware of these features in a timely manner, a huge chunk of these accidents can be avoided. This study proposes a computer vision-based solution for detecting and recognizing traffic types and signs to help drivers pave the door for self-driving cars. A real-world roadside dataset was collected under varying lighting and road conditions, and individual frames were annotated. Two deep learning models, YOLOv7 and Faster RCNN, were trained on this custom-collected dataset to detect the aforementioned road features. The models produced mean Average Precision (mAP) scores of 87.20% and 75.64%, respectively, along with class accuracies of over 98.80%; all of these were state-of-the-art. The proposed model provides an excellent benchmark to build on to help improve traffic situations and enable future technological advances, such as Advance Driver Assistance System (ADAS) and self-driving cars.

The NWRD Dataset: An Open-Source Annotated Segmentation Dataset of Diseased Wheat Crop.

Wheat stripe rust disease (WRD) is extremely detrimental to wheat crop health, and it severely affects the crop yield, increasing the risk of food insecurity. Manual inspection by trained personnel is carried out to inspect the disease spread and extent of damage to wheat fields. However, this is quite inefficient, time-consuming, and laborious, owing to the large area of wheat plantations. Artificial intelligence (AI) and deep learning (DL) offer efficient and accurate solutions to such real-world problems. By analyzing large amounts of data, AI algorithms can identify patterns that are difficult for humans to detect, enabling early disease detection and prevention. However, deep learning models are data-driven, and scarcity of data related to specific crop diseases is one major hindrance in developing models. To overcome this limitation, in this work, we introduce an annotated real-world semantic segmentation dataset named the NUST Wheat Rust Disease (NWRD) dataset. Multileaf images from wheat fields under various illumination conditions with complex backgrounds were collected, preprocessed, and manually annotated to construct a segmentation dataset specific to wheat stripe rust disease. Classification of WRD into different types and categories is a task that has been solved in the literature; however, semantic segmentation of wheat crops to identify the specific areas of plants and leaves affected by the disease remains a challenge. For this reason, in this work, we target semantic segmentation of WRD to estimate the extent of disease spread in wheat fields. Sections of fields where the disease is prevalent need to be segmented to ensure that the sick plants are quarantined and remedial actions are taken. This will consequently limit the use of harmful fungicides only on the targeted disease area instead of the majority of wheat fields, promoting environmentally friendly and sustainable farming solutions. Owing to the complexity of the proposed NWRD segmentation dataset, in our experiments, promising results were obtained using the UNet semantic segmentation model and the proposed adaptive patching with feedback (APF) technique, which produced a precision of 0.506, recall of 0.624, and F1 score of 0.557 for the rust class.

Genetic polymorphism of Β-casein gene and its association with milk production and composition in Azi-Kheli buffalo.

The aim of this study was to find out the genetic polymorphism in ß-casein gene CSN2 in Azi-Kheli buffaloes found in district Swat. Blood samples from 250 buffaloes were collected and processed in lab for sequencing to see the genetic polymorphism in CSN2 gene on 67 position of exon7. The ß-casein is a milk second abundant protein having some variants, wherein A1 and A2 are the most common. After performing sequence analysis, it was found that Azi-Kheli buffaloes were homozygous for only A2 type variant. The amino acid change (proline to histadine) on 67 position of exon 7 was not found; however, three other novel SNPs at loci g.20545A > G, g.20570G > A, and g.20693C > A were identified in the study. Amino acid change due to SNPs were found as SNP1, valine > proline; SNP2, leucin > phenylalanine; and SNP3, threonine > valine. Allelic and genotypic frequencies' analysis exhibited that all three SNPs were following the Hardy-Weinberg equilibrium (HWE: P < 0.05). All the three SNPs showed medium PIC value and gene heterozygosity. The SNPs located on different position of exon 7 of CSN2 gene exhibited associations with some of the performance traits and milk composition. Higher daily milk yield of 9.86 ± 0.43 L and the peak milk yield of 13.80 ± 0.60 L were found in response to SNP3 followed by SNP2 and SNP1. The percentage of milk fat and protein was found significantly higher (P ≤ 0.05) in relation to SNP3 followed by SNP2 and SNP1 given as 7.88 ± 0.41, 7.48 ± 0.33, and 7.15 ± 0.48 for fat% and 4.00 ± 0.15, 3.73 ± 0.10 and 3.40 ± 0.10 for protein%. It was concluded that Azi-Kheli buffalo milk contains A2 genetic variant along with other useful novel variants indicating quality milk for human health. Genotypes of SNP3 should be given preference in selection both in indices and nucleotide polymorphism.

Systematic analysis of HD-ZIP transcription factors in sesame genome and gene expression profiling of SiHD-ZIP class I entailing drought stress responses at early seedling stage.

BACKGROUND: Sesame is an ancient oilseed crop, known for its high oil content and quality. Its sensitivity to drought at early seedling stage is one of the limiting factors affecting its world-wide growth and productivity. Among plant specific transcription factors, the association of HD-ZIPs with sesame drought responses at early seedling stage is not well-established yet and is very important to develop our molecular understanding on sesame drought tolerance. METHODS AND RESULTS: In this study, total 61 sesame HD-ZIP proteins were identified, based on their protein sequence homology with Arabidopsis and protein domain(s) architecture prediction, followed by their phylogenetic, conserved domain(s) motifs and gene structure analyses to classify them into four classes (HD-ZIP Class I-IV). HD-ZIP Class I was also subdivided into four subgroups: α (SiHZ25, SiHZ43, SiHZ9 and SiHZ16), ß1 (SiHZ10, SiHZ30, SiHZ32 and SiHZ26), ß2 (SiHZ42 and SiHZ45) and γ (SiHZ17, SiHZ7 and SiHZ35) by a comparative phylogenetic analysis of sesame with Arabidopsis and maize. Afterwards, twenty-one days old sesame seedlings were exposed to drought stress by withholding water for 7 days (when soil moisture content reduced to ~16%) and gene expression of HD-ZIP Class I (13 members) was performed in well- watered (control) and drought stressed seedlings. The gene expression analysis showed that the expressions of SiHZ7 (6.8 fold) and SiHZ35 (2.6 fold) from γ subgroup were significantly high in drought seedlings. CONCLUSIONS: This study is useful in demonstrating the role of SiHD-ZIP Class I in sesame drought responses at early seedling stage and to develop its novel drought tolerant varieties.

LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI.

Brain-computer interface (BCI) systems based on functional near-infrared spectroscopy (fNIRS) have been used as a way of facilitating communication between the brain and peripheral devices. The BCI provides an option to improve the walking pattern of people with poor walking dysfunction, by applying a rehabilitation process. A state-of-the-art step-wise BCI system includes data acquisition, pre-processing, channel selection, feature extraction, and classification. In fNIRS-based BCI (fNIRS-BCI), channel selection plays a vital role in enhancing the classification accuracy of the BCI problem. In this study, the concentration of blood oxygenation (HbO) in a resting state and in a walking state was used to decode the walking activity and the resting state of the subject, using channel selection by Least Absolute Shrinkage and Selection Operator (LASSO) homotopy-based sparse representation classification. The fNIRS signals of nine subjects were collected from the left hemisphere of the primary motor cortex. The subjects performed the task of walking on a treadmill for 10 s, followed by a 20 s rest. Appropriate filters were applied to the collected signals to remove motion artifacts and physiological noises. LASSO homotopy-based sparse representation was used to select the most significant channels, and then classification was performed to identify walking and resting states. For comparison, the statistical spatial features of mean, peak, variance, and skewness, and their combination, were used for classification. The classification results after channel selection were then compared with the classification based on the extracted features. The classifiers used for both methods were linear discrimination analysis (LDA), support vector machine (SVM), and logistic regression (LR). The study found that LASSO homotopy-based sparse representation classification successfully discriminated between the walking and resting states, with a better average classification accuracy (p < 0.016) of 91.32%. This research provides a step forward in improving the classification accuracy of fNIRS-BCI systems. The proposed methodology may also be used for rehabilitation purposes, such as controlling wheelchairs and prostheses, as well as an active rehabilitation training technique for patients with motor dysfunction.

Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks.

This research presents a brain-computer interface (BCI) framework for brain signal classification using deep learning (DL) and machine learning (ML) approaches on functional near-infrared spectroscopy (fNIRS) signals. fNIRS signals of motor execution for walking and rest tasks are acquired from the primary motor cortex in the brain's left hemisphere for nine subjects. DL algorithms, including convolutional neural networks (CNNs), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM) are used to achieve average classification accuracies of 88.50%, 84.24%, and 85.13%, respectively. For comparison purposes, three conventional ML algorithms, support vector machine (SVM), k-nearest neighbor (k-NN), and linear discriminant analysis (LDA) are also used for classification, resulting in average classification accuracies of 73.91%, 74.24%, and 65.85%, respectively. This study successfully demonstrates that the enhanced performance of fNIRS-BCI can be achieved in terms of classification accuracy using DL approaches compared to conventional ML approaches. Furthermore, the control commands generated by these classifiers can be used to initiate and stop the gait cycle of the lower limb exoskeleton for gait rehabilitation.

Antibacterial Activity of Dental Composite with Ciprofloxacin Loaded Silver Nanoparticles.

Resin composites have been widely used in dental restoration. However, polymerization shrinkage and resultant bacterial microleakage are major limitations that may lead to secondary caries. To overcome this, a new type of antibacterial resin composite containing ciprofloxacin-loaded silver nanoparticles (CIP-AgNPs) were synthesized. The chemical reduction approach successfully produced CIP-AgNPs, as demonstrated by FTIR, zeta potential, scanning electron microscopy, and ultraviolet-visible (UV-vis) spectroscopy. CIP-AgNPs were added to resin composites and the antibacterial activity of the dental composite discs were realized against Enterococcus faecalis, Streptococcus mutans, and the Saliva microcosm. The biocompatibility of modified resin composites was assessed and mechanical testing of modified dental composites was also performed. The results indicated that the antibacterial activity and compressive strength of resin composites containing CIP-AgNPs were enhanced compared to the control group. They were also biocompatible when compared to resin composites containing AgNPs. In short, these results established strong ground application for CIP-AgNP-modified dental composite resins.

Two hours method for RNA and DNA co-extraction from blood of coronary artery disease patients: Fast, simple and economical technique.

Objectives: Extraction of DNA and RNA is the first step in genomics and transcriptomics studies. Phenol-chloroform method for DNA extraction has been the widely used method. However, this method is relatively expensive and time-consuming. The objective of the present study was to validate a cost and time-effective protocol that will reduce the burden of molecular biology-based research and make a difference in laboratories with limited resources. Methods: A comparative study was conducted at Syed Qamer Alam Research Laboratory, Shifa College of Medicine; from February, 2021 to August, 2021. TRIzol™ method was used to extract RNA from blood samples of coronary artery disease patients and remnant was used to extract DNA. The quantity, purity and integrity of the extracted DNA by both methods (TRIzol and phenol-chloroform) was examined. PCR product amplification was performed with thrombomodulin (THBD) gene to validate the characteristic of the extracted DNA and its efficiency for downstream experiments. Results: The DNA yield in the TRIzol™ method was three-fold higher than phenol chloroform method. Both methods showed intact genomic DNA on the agarose gel, and extracted DNA was efficient for PCR amplification. Conclusion: The TRIzol™ method for RNA and DNA co-extraction is fast, simple and economical technique. So, it can be adopted for routine molecular biology analyses in limited resources setup.

The interplay between the immune system and SARS-CoV-2 in COVID-19 patients.

Millions of people across the globe have been affected by coronavirus disease 2019 (COVID-19), which began in Wuhan, China, and is caused by SARS-CoV-2. COVID-19 has a variety of clinical characteristics and triggers immune responses required for the elimination of the viral agent. Currently, no effective treatment options are available for targeting SARS-CoV-2 infection. Repurposing of drugs such as chloroquine, thalidomide, and leflunomide alongside convalescent plasma is being employed as a therapeutic strategy. Clinical studies have shown that both asymptomatic and symptomatic patients can have an extremely active immune response that is largely attributable to immune system modulations. This includes cytokine storm syndrome (CSS), which affects the adaptive immune system, leading to exhaustion of natural killer (NK) cells and thrombocytopenia in some cases. This review examines the interaction of SARS-CoV-2 with the host immune system and the potential for the development of appropriate immunotherapy for the treatment of COVID-19.

Gene expression of thrombomodulin, TNF-α and NF-KB in coronary artery disease patients of Pakistan.

Thrombomodulin (THBD) is an endothelial surface glycoprotein receptor, having a pivotal role in maintaining laminar blood flow. It functions to protect endothelial integrity by exhibiting anti-coagulation and anti-inflammatory properties thereby playing a key role in cardiovascular disease (CVD) pathology. Cholesterol lowering drugs have shown to alter the anti-inflammatory effects of cytokines. Understanding the molecular aspects of THBD gene and its relation to inflammatory cytokines is important to identify new prognostic and therapeutic targets for the CVD treatments. The present study was conducted to measure the expression of THBD, TNF-α and NF-kB genes in coronary artery disease patients (CAD) in Pakistani population. Lipid profile and BMI was compared both on fifty CAD patients and fifty healthy individuals. Expression analysis for THBD, TNF-α and NF-kB was carried out using real time PCR. The effect of lipid lowering drugs on cardiometabolic risk variables especially gene expression was analyzed. Our results indicated that the difference in BMI was marginal; however LDL-cholesterol and triglycerides levels in CAD patients were significantly higher than healthy individuals. THBD gene was significantly up-regulated whereas TNF-α and NF-kB were significantly down regulated in CAD individuals. Further exploration revealed that these variations were accounted to the use of statins by the patients. The use of statins by CAD patients up-regulated the mRNA expression of THBD by down-regulation of inflammatory mediators. The enhanced expression of endothelial THBD in response to cholesterol lowering drugs establishes a novel pleiotropic target that can be of clinical significance in thromboembolic and inflammatory disorders.

A Novel Roll and Pitch Estimation Approach for a Ground Vehicle Stability Improvement Using a Low Cost IMU.

Onboard attitude estimation for a ground vehicle is persuaded by its application in active anti-roll bar design. Conventionally, the attitude estimation problem for a ground vehicle is a complex one, and computationally, its solution is very intensive. Lateral load transfer is an important parameter which should be taken in account for all roll stability control systems. This parameter is directly related to vehicle roll angle, which can be measured using devices such as dual antenna global positioning system (GPS) which is a costly technique, and this led to the current work in which we developed a simple and robust attitude estimation technique that is tested on a ground vehicle for roll mitigation. In the first phase Luenberger and Sliding mode observer is implemented using simplest roll dynamics model to measure the roll angle of a vehicle and the validation of results is carried using commercial software, CarSim® (CarSim, Ann Arbor, MI, USA). In the second phase of research, complementary and Kalman filters have been designed for attitude estimation. In the third phase, a low-cost inertial measurement unit (IMU) is mounted on a vehicle, and both the complementary filter (CF) and Kalman filter (KF) are applied independently to measure the data for both smooth and uneven terrains at four different frequencies. We compared the simulated and real-time results of roll and pitch angles obtained using the complementary and Kalman filters. Using the proposed method, the achieved root mean square error (RMSE) is less than 0.73 degree for pitch and 0.68 degree for roll, with a sample time of 2 ms. Thus, a warning signal can be generated to mitigate roll over. Hence, we claim that our proposed method can provide a low-cost solution to the roll-over problem for a road vehicle.

Enhancing Classification Performance of fNIRS-BCI by Identifying Cortically Active Channels Using the z-Score Method.

A state-of-the-art brain-computer interface (BCI) system includes brain signal acquisition, noise removal, channel selection, feature extraction, classification, and an application interface. In functional near-infrared spectroscopy-based BCI (fNIRS-BCI) channel selection may enhance classification performance by identifying suitable brain regions that contain brain activity. In this study, the z-score method for channel selection is proposed to improve fNIRS-BCI performance. The proposed method uses cross-correlation to match the similarity between desired and recorded brain activity signals, followed by forming a vector of each channel's correlation coefficients' maximum values. After that, the z-score is calculated for each value of that vector. A channel is selected based on a positive z-score value. The proposed method is applied to an open-access dataset containing mental arithmetic (MA) and motor imagery (MI) tasks for twenty-nine subjects. The proposed method is compared with the conventional t-value method and with no channel selected, i.e., using all channels. The z-score method yielded significantly improved (p < 0.0167) classification accuracies of 87.2 ± 7.0%, 88.4 ± 6.2%, and 88.1 ± 6.9% for left motor imagery (LMI) vs. rest, right motor imagery (RMI) vs. rest, and mental arithmetic (MA) vs. rest, respectively. The proposed method is also validated on an open-access database of 17 subjects, containing right-hand finger tapping (RFT), left-hand finger tapping (LFT), and dominant side foot tapping (FT) tasks.The study shows an enhanced performance of the z-score method over the t-value method as an advancement in efforts to improve state-of-the-art fNIRS-BCI systems' performance.

Computational design of anti-cancer peptides tailored to target specific tumor markers.

Anti-cancer peptides (ACPs) are short peptides known for their ability to inhibit tumor cell proliferation, migration, and the formation of tumor blood vessels. In this study, we designed ACPs to target receptors often overexpressed in cancer using a systematic in silico approach. Three target receptors (CXCR1, DcR3, and OPG) were selected for their significant roles in cancer pathogenesis and tumor cell proliferation. Our peptide design strategy involved identifying interacting residues (IR) of these receptors, with their natural ligands serving as a reference for designing peptides specific to each receptor. The natural ligands of these receptors, including IL8 for CXCR1, TL1A for DcR3, and RANKL for OPG, were identified from the literature. Using the identified interacting residues (IR), we generated a peptide library through simple permutation and predicted the structure of each peptide. All peptides were analyzed using the web-based prediction server for Anticancer peptides, AntiCP. Docking simulations were then conducted to analyze the binding efficiencies of peptides with their respective target receptors, using VEGA ZZ and Chimera for interaction analysis. Our analysis identified HPKFIKELR as the interacting residues (IR) of CXCR-IL8. For DcR3, we utilized three domains from TL1A (TDSYPEP, TKEDKTF, LGLAFTK) as templates, along with two regions (SIKIPSS and PDQDATYP) from RANKL, to generate a library of peptide analogs. Subsequently, peptides for each receptor were shortlisted based on their predicted anticancer properties as determined by AntiCP and were subjected to docking analysis. After docking, peptides that exhibited the least binding energy were further analyzed for their detailed interaction with their respective receptors. Among these, peptides C9 (HPKFELY) and C7 (HPKFEWL) for CXCR1, peptides D6 (ADSYPQP) and D18 (AFSYPFP) for DcR3, and peptides P19 (PDTYPQDP) and p16 (PDQDATYP) for OPG, demonstrated the highest affinity and stronger interactions compared to the other peptides. Although in silico predictions indicated a favorable binding affinity of the designed peptides with target receptors, further experimental validation is essential to confirm their binding affinity, stability and pharmacokinetic characteristics.

Clinical relevance of circulating non-coding RNAs in metabolic diseases: Emphasis on obesity, diabetes, cardiovascular diseases and metabolic syndrome.

Non-coding RNAs (ncRNAs) participate in the regulation of several cellular processes including transcription, RNA processing and genome rearrangement. The aberrant expression of ncRNAs is associated with several pathological conditions. In this review, we focused on recent information to elucidate the role of various regulatory ncRNAs i.e., micro RNAs (miRNAs), circular RNAs (circRNAs) and long-chain non-coding RNAs (lncRNAs), in metabolic diseases, e.g., obesity, diabetes mellitus (DM), cardiovascular diseases (CVD) and metabolic syndrome (MetS). The mechanisms by which ncRNAs participated in disease pathophysiology were also highlighted. miRNAs regulate the expression of genes at transcriptional and translational levels. circRNAs modulate the regulation of gene expression via miRNA sponging activity, interacting with RNA binding protein and polymerase II transcription regulation. lncRNAs regulate the expression of genes by acting as a protein decoy, miRNA sponging, miRNA host gene, binding to miRNA response elements (MRE) and the recruitment of transcriptional element or chromatin modifiers. We examined the role of ncRNAs in the disease pathogenesis and their potential role as molecular markers for diagnosis, prognosis and therapeutic targets. We showed the involvement of ncRNAs in the onset of obesity and its progression to MetS and CVD. miRNA-192, miRNA-122, and miRNA-221 were dysregulated in all these metabolic diseases. Other ncRNAs, implicated in at least three diseases include miRNA-15a, miRNA-26, miRNA-27a, miRNA-320, and miRNA-375. Dysregulation of ncRNAs increased the risk of development of DM and MetS and its progression to CVD in obese individuals. Hence, these molecules are potential targets to arrest or delay the progression of metabolic diseases.

A concise overview of advancements in ultrasensitive biosensor development.

Electrochemical biosensing has evolved as a diverse and potent method for detecting and analyzing biological entities ranging from tiny molecules to large macromolecules. Electrochemical biosensors are a desirable option in a variety of industries, including healthcare, environmental monitoring, and food safety, due to significant advancements in sensitivity, selectivity, and portability brought about by the integration of electrochemical techniques with nanomaterials, bio-recognition components, and microfluidics. In this review, we discussed the realm of electrochemical sensors, investigating and contrasting the diverse strategies that have been harnessed to push the boundaries of the limit of detection and achieve miniaturization. Furthermore, we assessed distinct electrochemical sensing methods employed in detection such as potentiometers, amperometers, conductometers, colorimeters, transistors, and electrical impedance spectroscopy to gauge their performance in various contexts. This article offers a panoramic view of strategies aimed at augmenting the limit of detection (LOD) of electrochemical sensors. The role of nanomaterials in shaping the capabilities of these sensors is examined in detail, accompanied by insights into the chemical modifications that enhance their functionality. Furthermore, our work not only offers a comprehensive strategic framework but also delineates the advanced methodologies employed in the development of electrochemical biosensors. This equips researchers with the knowledge required to develop more accurate and efficient detection technologies.

Computational Analysis Reveals Distinctive Interaction of miRNAs with Target Genes in the Pathogenesis of Chronic Kidney Disease.

The regulation of genes is crucial for maintaining a healthy intracellular environment, and any dysregulation of gene expression leads to several pathological complications. It is known that many diseases, including kidney diseases, are regulated by miRNAs. However, the data on the use of miRNAs as biomarkers for the diagnosis and treatment of chronic kidney disease (CKD) are not conclusive. The purpose of this study was to elucidate the potential of miRNAs as an efficient biomarker for the detection and treatment of CKD at its early stages. Gene expression profiling data were acquired from the Gene Expression Omnibus (GEO) and differentially expressed genes (DEGs) were identified. miRNAs directly associated with CKD were obtained from an extensive literature search. Network illustration of miRNAs and their projected target differentially expressed genes (tDEGs) was accomplished, followed by functional enrichment analysis. hsa-miR-1-3p, hsa-miR-206, hsa-miR-494 and hsa-miR-577 exhibited a strong association with CKD through the regulation of genes involved in signal transduction, cell proliferation, the regulation of transcription and apoptotic process. All these miRNAs have shown significant contributions to the inflammatory response and the processes which eventually lead to the pathogenesis of CKD. The in silico approach used in this research represents a comprehensive analysis of identified miRNAs and their target genes for the identification of molecular markers of disease processes. The outcomes of the study recommend further efforts for developing miRNA biomarkers set for the early diagnosis of CKD.