Machine learning for synergistic network pharmacology: a comprehensive overview.

Network pharmacology is an emerging area of systematic drug research that attempts to understand drug actions and interactions with multiple targets. Network pharmacology has changed the paradigm from 'one-target one-drug' to highly potent 'multi-target drug'. Despite that, this synergistic approach is currently facing many challenges particularly mining effective information such as drug targets, mechanism of action, and drug and organism interaction from massive, heterogeneous data. To overcome bottlenecks in multi-target drug discovery, computational algorithms are highly welcomed by scientific community. Machine learning (ML) and especially its subfield deep learning (DL) have seen impressive advances. Techniques developed within these fields are now able to analyze and learn from huge amounts of data in disparate formats. In terms of network pharmacology, ML can improve discovery and decision making from big data. Opportunities to apply ML occur in all stages of network pharmacology research. Examples include screening of biologically active small molecules, target identification, metabolic pathways identification, protein-protein interaction network analysis, hub gene analysis and finding binding affinity between compounds and target proteins. This review summarizes the premier algorithmic concepts of ML in network pharmacology and forecasts future opportunities, potential applications as well as several remaining challenges of implementing ML in network pharmacology. To our knowledge, this study provides the first comprehensive assessment of ML approaches in network pharmacology, and we hope that it encourages additional efforts toward the development and acceptance of network pharmacology in the pharmaceutical industry.

Phase Engineered Composite Phase Change Materials for Thermal Energy Manipulation.

Phase change materials (PCMs) present a dual thermal management functionality through intrinsic thermal energy storage (TES) capabilities while maintaining a constant temperature. However, the practical application of PCMs encounters challenges, primarily stemming from their low thermal conductivity and shape-stability issues. Despite significant progress in the development of solid-solid PCMs, which offer superior shape-stability compared to their solid-liquid counterparts, they compromise TES capacity. Herein, a universal phase engineering strategy is introduced to address these challenges. The approach involves compositing solid-liquid PCM with a particulate-based conductive matrix followed by surface reaction to form a solid-solid PCM shell, resulting in a core-shell composite with enhanced thermal conductivity, high thermal storage capacity, and optimal shape-stability. The core-shell structure designed in this manner not only encapsulates the energy-rich solid-liquid PCM core but also significantly enhances TES capacity by up to 52% compared to solid-solid PCM counterparts. The phase-engineered high-performance PCMs exhibit excellent thermal management capabilities by reducing battery cell temperature by 15 °C and demonstrating durable solar-thermal-electric power generation under cloudy or no sunshine conditions. This proposed strategy holds promise for extending to other functional PCMs, offering a compelling avenue for the development of high-performance PCMs for thermal energy applications.

A Deep Dive into Cu2ZnSnS4 (CZTS) Solar Cells: A Review of Exploring Roadblocks, Breakthroughs, and Shaping the Future.

Renewable energy is crucial for sustainable future, and Cu2ZnSnS4 (CZTS) based solar cells shine as a beacon of hope. CZTS, composed of abundant, low-cost, and non-toxic elements, shares similarities with Cu(In,Ga)Se2 (CIGS). However, despite its promise and appealing properties for solar cells, CZTS-based solar cells faces performance challenges owing to inherent issues with CZTS material, and conventional substrate structure complexities. This review critically examines these roadblocks, explores ongoing efforts and breakthroughs, providing insight into the evolving landscape of CZTS-based solar cells research. Furthermore, as an optimistic turn in the field, the review first highlights the crucial need to transition to a superstrate structure for CZTS-based single junction devices, and summarizes the substantial progress made in this direction. Subsequently, dive into the discussion about the fascinating realm of CZTS-based tandem devices, providing an overview of the existing literature as well as outlining the possible potential strategies for enhancing the efficiency of such devices. Finally, the review provides a useful outlook that outlines the priorities for future research and suggesting where efforts should concentrate to shape the future of CZTS-based solar cells.

Bio-net dataset: AI-based diagnostic solutions using peripheral blood smear images.

Peripheral blood smear examination is one of the basic steps in the evaluation of different blood cells. It is a confirmatory step after an automated complete blood count analysis. Manual microscopy is time-consuming and requires professional laboratory expertise. Therefore, the turn-around time for peripheral smear in a health care center is approximately 3-4 hours. To avoid the traditional method of manual counting under the microscope a computerized automation of peripheral blood smear examination has been adopted, which is a challenging task in medical diagnostics. In recent times, deep learning techniques have overcome the challenges associated with human microscopic evaluation of peripheral smears and this has led to reduced cost and precise diagnosis. However, their application can be significantly improved by the availability of annotated datasets. This study presents a large customized annotated blood cell dataset (named the Bio-Net dataset from healthy individuals) and blood cell detection and counting in the peripheral blood smear images. A mini-version of the dataset for specialized WBC-based image processing tasks is also equipped to classify the healthy and mature WBCs in their respective classes. An object detection algorithm called You Only Look Once (YOLO) with a refashion disposition has been trained on the novel dataset to automatically detect and classify blood cells into RBCs, WBCs, and platelets and compare the results with other publicly available datasets to highlight the versatility. In short the introduction of the Bio-Net dataset and AI-powered detection and counting offers a significant potential for advancement in biomedical research for analyzing and understanding biological data.

Cardiovascular Health and Disease in the Pakistani American Population.

PURPOSE OF REVIEW: This narrative review seeks to elucidate clinical and social factors influencing cardiovascular health, explore the challenges and potential solutions for enhancing cardiovascular health, and identify areas where further research is needed to better understand cardiovascular issues in native and American Pakistani populations. RECENT FINDINGS: The prevalence of cardiometabolic disease is high not only in Pakistan but also among its global diaspora. This situation is further complicated by the inadequacy of current cardiovascular risk assessment tools, which often fall short of accurately gauging the risk among Pakistani individuals, underscoring the urgent need for more tailored and effective assessment methodologies. Moreover, social determinants play a crucial role in shaping cardiovascular health. The burden of cardiovascular disease and upstream risk factors is high among American Pakistani individuals. Future research is needed to better understand the heightened risk of cardiovascular disease among Pakistani individuals.

Optimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks.

A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions.

Performance evaluation of various techniques in estimating precipitation record of a sparsely gauged mountainous watershed.

Comprehensive precipitation data is essential for hydrological, agricultural, and climatological studies. Yet, gaps and sparse rain gauge distribution pose challenges, requiring imputation algorithms to fill data gaps. The aim of this research is to evaluate the performance of several approaches for estimating incomplete precipitation data in the Upper Indus Basin (UIB). Eight various imputation approaches were used on sparsely gauged mountainous UIB on a monthly time series of twenty-four meteorological observatories. Following that, the estimation approaches were evaluated using a rank-based approach comprising four different statistical indicators. The results indicate that multiple linear regression is the best-performing strategy for most of the stations regardless of season or orography, followed by the arithmetic average method and inverse distance weighing method.

Prospects of antimicrobial peptides as an alternative to chemical preservatives for food safety.

Antimicrobial peptides (AMPs) are a potential alternative to antimicrobial agents that have got considerable research interest owing to their significant role in the inhibition of bacterial pathogens. These AMPs can essentially inhibit the growth and multiplication of microbes through multiple mechanisms including disruption of cellular membranes, inhibition of cell wall biosynthesis, or affecting intracellular components and cell division. Moreover, AMPs are biocompatible and biodegradable therefore, they can be a good alternative to antimicrobial agents and chemical preservatives. A few of their features for example thermostability and high selectivity are quite appealing for their potential use in the food industry for food preservation to prevent the spoilage caused by microorganisms and foodborne pathogens. Despite these advantages, very few AMPs are being used at an industrial scale for food preservation as these peptides are quite vulnerable to external environmental factors which deter their practical applications and commercialization. The review aims to provide an outline of the mechanism of action of AMPs and their prospects as an alternative to chemical preservatives in the food industry. Further studies related to the structure-activity relationship of AMPs will help to expand the understanding of their mechanism of action and to determine specific conditions to increase their stability and applicability in food preservation.

A Comprehensive Update of Various Attempts by Medicinal Chemists to Combat COVID-19 through Natural Products.

The ongoing COVID-19 pandemic has resulted in a global panic because of its continual evolution and recurring spikes. This serious malignancy is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the outbreak, millions of people have been affected from December 2019 till now, which has led to a great surge in finding treatments. Despite trying to handle the pandemic with the repurposing of some drugs, such as chloroquine, hydroxychloroquine, remdesivir, lopinavir, ivermectin, etc., against COVID-19, the SARS-CoV-2 virus continues its out-of-control spread. There is a dire need to identify a new regimen of natural products to combat the deadly viral disease. This article deals with the literature reports to date of natural products showing inhibitory activity towards SARS-CoV-2 through different approaches, such as in vivo, in vitro, and in silico studies. Natural compounds targeting the proteins of SARS-CoV-2-the main protease (Mpro), papain-like protease (PLpro), spike proteins, RNA-dependent RNA polymerase (RdRp), endoribonuclease, exoribonuclease, helicase, nucleocapsid, methyltransferase, adeno diphosphate (ADP) phosphatase, other nonstructural proteins, and envelope proteins-were extracted mainly from plants, and some were isolated from bacteria, algae, fungi, and a few marine organisms.

Graded 2D/3D Perovskite Hetero-Structured Films with Suppressed Interfacial Recombination for Efficient and Stable Solar Cells via DABr Treatment.

Several strategies and approaches have been reported for improving the resilience and optoelectronic properties of perovskite films. However, fabricating a desirable and stable perovskite absorber layer is still a great challenge due to the optoelectronic and fabrication limitations of the materials. Here, we introduce diethylammonium bromide (DABr) as a post-treatment material for the pre-deposited methylammonium lead iodide (MAPbI3) film to fabricate a high-quality two-dimensional/three-dimensional (2D/3D) stacked hetero-structure perovskite film. The post-treatment method of DABr not only induces the small crystals of MAPbI3 perovskite secondary growth into a large crystal, but also forms a 2D capping layer on the surface of the 3D MAPbI3 film. Meanwhile, the grains and crystallization of 3D film with DABr post-treatment are significantly improved, and the surface defect density is remarkably reduced, which in turn effectively suppressed the charge recombination in the interface between the perovskite layer and the charge transport layer. The perovskite solar cell based on the DABr-treatment exhibited a significantly enhanced power conversion efficiency (PCE) of 19.10% with a notable improvement in the open circuit voltage (VOC) of 1.06 V and good stability, advocating the potential of this perovskite post-treatment approach.

Future climate projections using the LARS-WG6 downscaling model over Upper Indus Basin, Pakistan.

This study investigates the projections of precipitation and temperature at the local scale in the Upper Indus Basin (UIB) in Pakistan using six Regional Climate Models (RCMs) from CORDEX under two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). For twenty-four stations spread across the study area, the Long Ashton Research Station Weather Generator, version six (LARS-WG6), was used to downscale the daily data from the six different RCMs for maximum temperature (Tmax), minimum temperature (Tmin), and precipitation (pr) at a spatial resolution of 0.44°. Investigations were made to predict changes in mean annual values of Tmax, Tmin, and precipitation during two future periods, i.e., the mid-century (2041-2070) and end-century (2071-2100). The model results from statistical and graphical comparison validated that the LARS-WG6 can simulate the temperature and the precipitation in the UIB. Each of the six RCMs and their ensemble revealed a continuously increased temperature projection in the basin; nevertheless, there is variation in projected magnitude across RCMs and between RCPs. The rise in average Tmax and Tmin was more significant under RCP 8.5 than RCP 4.5, possibly due to unmitigated greenhouse gas emissions (GHGs). The precipitation projections follow the non-uniform trend, i.e., not all RCMs agree on whether the precipitation will increase or decrease in the basin, and no orderly variations were detected during any future periods under any RCP. However, an overall increase in precipitation is projected by the ensemble of RCMs.

Associations between lung function and physical and cognitive health in the Canadian Longitudinal Study on Aging (CLSA): A cross-sectional study from a multicenter national cohort.

BACKGROUND: Low lung function is associated with high mortality and adverse cardiopulmonary outcomes. Less is known of its association with broader health indices such as self-reported respiratory symptoms, perceived general health, and cognitive and physical performance. The present study seeks to address the association between forced expiratory volume in 1 second (FEV1), an indicator of lung function, with broad markers of general health, relevant to aging trajectory in the general population. METHODS AND FINDINGS: From the Canadian general population, 22,822 adults (58% females, mean age 58.8 years [standard deviation (SD) 9.6]) were enrolled from the community between June 2012 and April 2015 from 11 Canadian cities and 7 provinces. Mixed effects regression was used to assess the cross-sectional relationship between FEV1 with self-reported respiratory symptoms, perceived poor general health, and cognitive and physical performance. All associations were adjusted for age, sex, body mass index (BMI), education, smoking status, and self-reported comorbidities and expressed as adjusted odds ratios (aORs). Based on the Global Lung Function Initiative (GLI) reference values, 38% (n = 8,626) had normal FEV1 (z-scores >0), 37% (n = 8,514) mild (z-score 0 to > -1 SD), 19% (n = 4,353) moderate (z-score -1 to > -2 SD), and 6% (n = 1,329) severely low FEV1 (z-score = < -2 SD). There was a graded association between lower FEV1 with higher aOR [95% CI] of self-reported moderate to severe respiratory symptoms (mild FEV1 1.09 [0.99 to 1.20] p = 0.08, moderate 1.45 [1.28 to 1.63] p < 0.001, and severe 2.67 [2.21 to 3.23] p < 0.001]), perceived poor health (mild 1.07 [0.9 to 1.27] p = 0.45, moderate 1.48 [1.24 to 1.78] p = <0.001, and severe 1.82 [1.42 to 2.33] p < 0.001]), and impaired cognitive performance (mild 1.03 [0.95 to 1.12] p = 0.41, moderate 1.16 [1.04 to 1.28] p < 0.001, and severe 1.40 [1.19 to 1.64] p < 0.001]). Similar graded association was observed between lower FEV1 with lower physical performance on gait speed, Timed Up and Go (TUG) test, standing balance, and handgrip strength. These associations were consistent across different strata by age, sex, tobacco smoking, obstructive, and nonobstructive impairment on spirometry. A limitation of the current study is the observational nature of these findings and that causality cannot be inferred. CONCLUSIONS: We observed graded associations between lower FEV1 with higher odds of disabling respiratory symptoms, perceived poor general health, and lower cognitive and physical performance. These findings support the broader implications of measured lung function on general health and aging trajectory.

Matching Charge Extraction Contact for Infrared PbS Colloidal Quantum Dot Solar Cells.

Infrared solar cells (IRSCs) can supplement silicon or perovskite SCs to broaden the utilization of the solar spectrum. As an ideal infrared photovoltaic material, PbS colloidal quantum dots (CQDs) with tunable bandgaps can make good use of solar energy, especially the infrared region. However, as the QD size increases, the energy level shrinking and surface facet evolution makes us reconsider the matching charge extraction contacts and the QD passivation strategy. Herein, different to the traditional sol-gel ZnO layer, energy-level aligned ZnO thin film from a magnetron sputtering method is adopted for electron extraction. In addition, a modified hybrid ligand recipe is developed for the facet passivation of large size QDs. As a result, the champion IRSC delivers an open circuit voltage of 0.49 V and a power conversion efficiency (PCE) of 10.47% under AM1.5 full-spectrum illumination, and the certified PCE is over 10%. Especially the 1100 nm filtered efficiency achieves 1.23%. The obtained devices also show high storage stability. The present matched electron extraction and QD passivation strategies are expected to highly booster the IR conversion yield and promote the fast development of new conception QD optoelectronics.

Impact of Hyperglycemia on Cardiovascular Events and Clinical Outcomes in Patients Hospitalized With COVID-19 Pneumonia.

OBJECTIVE: To study cardiovascular events and clinical outcomes in patients with elevated glycated hemoglobin (HbA1c) levels and/or admission hyperglycemia and those with type 2 diabetes hospitalized with SARS-CoV-2 pneumonia. METHODS: This was a multicenter retrospective study of 1645 patients hospitalized with SARS-CoV-2 pneumonia. Diagnosis of SARS-CoV-2 pneumonia required a positive reverse transcription-polymerase chain reaction result for SARS-CoV-2, presence of new or worsening pulmonary infiltrates on computed tomography scan or chest x-ray, and at least one of following: (1) new or increased cough, (2) temperature of >37.8 °C, or (3) dyspnea. Outcomes included in-hospital cardiovascular events, intensive care unit admission, and mortality. Logistic regression was used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for association of elevated HbA1c levels and/or admission hyperglycemia and type 2 diabetes for individual outcomes. RESULTS: Among 1645 adults hospitalized with SARS-CoV-2 pneumonia, 18 with type 1 diabetes were excluded from the analysis. Of 1627 adults, 634 (39%) had known diagnosis of type 2 diabetes, and among 993 patients with no diabetes, 107 (10.8%) patients were identified with elevated HbA1c levels and/or admission hyperglycemia. Patients with elevated HbA1c levels and/or admission hyperglycemia had increased odds of developing acute in-hospital cardiovascular events (OR, 1.73; 95% CI, 1.07-2.80), intensive care unit admissions (OR, 1.61; 95% CI, 1.10-2.34), and mortality (OR, 1.77; 95% CI, 1.02-3.07) compared to patients with type 2 diabetes and no diabetes. CONCLUSION: Patients with elevated HbA1c levels and/or admission hyperglycemia hospitalized with SARS-CoV-2 pneumonia have increased risk of developing acute in-hospital cardiovascular complications and overall poor clinical outcomes compared with patients with type 2 diabetes and no diabetes.

Integrated System Pharmacology Approaches to Elucidate Multi-Target Mechanism of Solanum surattense against Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignant liver tumors with high mortality. Chronic hepatitis B and C viruses, aflatoxins, and alcohol are among the most common causes of hepatocellular carcinoma. The limited reported data and multiple spectra of pathophysiological mechanisms of HCC make it a challenging task and a serious economic burden in health care management. Solanum surattense (S. surattense) is the herbal plant used in many regions of Asia to treat many disorders including various types of cancer. Previous in vitro studies revealed the medicinal importance of S. surattense against hepatocellular carcinoma. However, the exact molecular mechanism of S. surattense against HCC still remains unclear. In vitro and in silico experiments were performed to find the molecular mechanism of S. surattense against HCC. In this study, the network pharmacology approach was used, through which multi-targeted mechanisms of S. surattense were explored against HCC. Active ingredients and potential targets of S. surattense found in HCC were figured out. Furthermore, the molecular docking technique was employed for the validation of the successful activity of bioactive constituents against potential genes of HCC. The present study investigated the active "constituent-target-pathway" networks and determined the tumor necrosis factor (TNF), epidermal growth factor receptor (EGFR), mammalian target of rapamycin (mTOR), Bcl-2-like protein 1(BCL2L1), estrogen receptor (ER), GTPase HRas, hypoxia-inducible factor 1-alpha (HIF1-α), Harvey Rat sarcoma virus, also known as transforming protein p21 (HRAS), and AKT Serine/Threonine Kinase 1 (AKT1), and found that the genes were influenced by active ingredients of S. surattense. In vitro analysis was also performed to check the anti-cancerous activity of S. surattense on human liver cells. The result showed that S. surattense appeared to act on HCC via modulating different molecular functions, many biological processes, and potential targets implicated in 11 different pathways. Furthermore, molecular docking was employed to validate the successful activity of the active compounds against potential targets. The results showed that quercetin was successfully docked to inhibit the potential targets of HCC. This study indicates that active constituents of S. surattense and their therapeutic targets are responsible for their pharmacological activities and possible molecular mechanisms for treating HCC. Lastly, it is concluded that active compounds of S. surattense act on potential genes along with their influencing pathways to give a network analysis in system pharmacology, which has a vital role in the development and utilization of drugs. The current study lays a framework for further experimental research and widens the clinical usage of S. surattense.

Synthesis of Novel N-Methylmorpholine-Substituted Benzimidazolium Salts as Potential α-Glucosidase Inhibitors.

The α-glucosidase enzyme, located in the brush border of the small intestine, is responsible for overall glycemic control in the body. It hydrolyses the 1,4-linkage in the carbohydrates to form blood-absorbable monosaccharides that ultimately increase the blood glucose level. α-Glucosidase inhibitors (AGIs) can reduce hydrolytic activity and help to control type 2 diabetes. Aiming to achieve this, a novel series of 1-benzyl-3-((2-substitutedphenyl)amino)-2-oxoethyl)-2-(morpholinomethyl)-1H-benzimidazol-3-ium chloride was synthesized and screened for its α-glucosidase inhibitory potential. Compounds 5d, 5f, 5g, 5h and 5k exhibited better α-glucosidase inhibitions compared to the standard drug (acarbose IC50 = 58.8 ± 0.012 µM) with IC50 values of 15 ± 0.030, 19 ± 0.060, 25 ± 0.106, 21 ± 0.07 and 26 ± 0.035 µM, respectively. Furthermore, the molecular docking studies explored the mechanism of enzyme inhibitions by different 1,2,3-trisubstituted benzimidazolium salts via significant ligand-receptor interactions.

Integrating Pharmacological and Computational Approaches for the Phytochemical Analysis of Syzygium cumini and Its Anti-Diabetic Potential.

Diabetes mellitus (DM) is a metabolic disease caused by improper insulin secretion leading to hyperglycemia. Syzygium cumini has excellent therapeutic properties due to its high levels of phytochemicals. The current research aimed to evaluate the anti-diabetic potential of S. cumini plant's seeds and the top two phytochemicals (kaempferol and gallic acid) were selected for further analysis. These phytochemicals were selected via computational tools and evaluated for α-Glucosidase inhibitory activity via enzymatic assay. Gallic acid (IC50 0.37 µM) and kaempferol (IC50 0.87 µM) have shown a stronger α-glucosidase inhibitory capacity than acarbose (5.26 µM). In addition, these phytochemicals demonstrated the highest binding energy, hydrogen bonding, protein-ligand interaction and the best MD simulation results at 100 ns compared to acarbose. Furthermore, the ADMET properties of gallic acid and kaempferol also fulfilled the safety criteria. Thus, it was concluded that S. cumini could potentially be used to treat DM. The potential bioactive molecules identified in this study (kaempferol and gallic acid) may be used as lead drugs against diabetes.

Role of Heavy Metals in Diabetes: Mechanisms and Treatment Strategies.

Toxic metals affecting metabolic pathways have a broad range in the ecosystem from both natural and anthropogenic sources. Because of constant contamination from waste and untreated chemical effluents, their emissions have risen significantly over the last few decades, quickly gaining attention due to their crucial role in the development of several metabolic disorders, notably diabetes mellitus. Cadmium and arsenic not only spread widely in our atmosphere but are also linked to a wide range of health hazards. These are primarily accumulated in the liver, kidney, and pancreas once they reach the human body, where they have deleterious effects on the metabolism of glucose and its association with other metabolic pathways, particularly glycolysis, glycogenesis, and gluconeogenesis, by altering and impairing the specific activity of major enzymes. Impairment of hepatic glucose homeostasis plays a crucial role in diabetes mellitus pathogenesis. Impaired liver and kidney functions, as well as decreased pancreatic and muscle function, also contribute significantly to elevated levels of blood glucose. Heavy metals have the potential to cause changes in the conformation in these enzymes. They also impair hormonal balance by destroying the pancreas and adrenal glands. Such metals often facilitate the development of reactive oxygen species and inhibit antioxidant defense mechanisms, with multiple organs subsequently damaged. This review briefly discusses the involvement of heavy metals in metabolic disorders such as diabetes mellitus, the enzymes involved in this pathway, and glucose homeostasis.

Pathogenesis of Diabetic Cardiomyopathy and Role of miRNA.

Diabetic cardiomyopathy is characterized as abnormal function and structure of myocardium associated with diabetes irrespective of other cardiac risk factors like hypertension or coronary artery disease (CAD). The pathogenesis of DCM was not well understood in the past due to its complexity but it has been discovered recently. Various factors are found to be associated with the onset of DCM including impaired calcium handling, remodeling of extracellular matrix (ECM), increased oxidative stress, altered metabolism, mitochondrial dysfunction, and endothelial dysfunction. Micro-RNAs (miRNAs) are also found to be of great importance in the pathogenesis of DCM. Different miRNAs like miR-126, miR-24, miR-1, miR-155, miR-499, and miR-199a are found to be associated with different types of heart diseases like CAD and myocardial infarction. Studies have shown that the miRNA plays a crucial role in the development of DCM and it was found that the expression levels of different miRNAs differ in patients as compared to healthy individuals. This review focuses on the pathogenesis of DCM and various factors involved in the onset of diabetic car-diomyopathy. Moreover, the probable role of miRNA in the pathogenesis of DCM is also discussed.

Role of Chewing Gum in Reducing Postoperative Ileus after Reversal of Ileostomy: A Randomized Controlled Trial.

BACKGROUND: Postoperative ileus is one of the most prevalent and troublesome problems after any elective or emergency laparotomy. Gum chewing has emerged as a new and simple modality for decreasing postoperative ileus. The aim of this study was to determine the effectiveness of chewing gum in reducing postoperative ileus in terms of passage of flatus and total length of hospital stay. PATIENTS AND METHODS: This single-blinded, randomized clinical trial was conducted in department of surgery, Services Hospital Lahore, between November 2013 and November 2015. The patients were divided into two groups: chewing gum (Group A) and no chewing gum (Group B). Starting 6 h after the operation, Group A patients were asked to chew gum for 30 min every 8 h; bowel sounds, passage of flatus and total length of hospital stay were noted. Outcome measures such as passage of flatus and total length of hospital stay in patients undergoing reversal of ileostomy were compared using t-test. RESULTS: Mean age of the patients in Group A was 26.12 (± 7.1) years and in Group B was 28.80 (± 10.5) years. There were 25 males (50%) and 25 females (50%) in Group A. In Group B, there were 29 males (58%) and 21 females (42%). Mean BMI in Group A was 23.5 (± 5.3), and in Group B was 21.4 (± 4.6). The mean time to pass flatus was noted to be significantly shorter, 18.36 (± 8.43) hours, in the chewing group (Group A), whereas in the no chewing gum group (Group B), it was 41.16 (± 6.14) hours (p value < 0.001). The mean length of hospital stay was significantly shorter 84 (± 8.3) hours in the chewing gum group (Group A) as compared to 107.04 (± 6.4) hours in the no chewing gum group (Group B) (p value 0.000). CONCLUSION: It is concluded that postoperative chewing of gum after the reversal of ileostomy is accompanied with a significantly shorter time to passage of flatus and shorter length of hospital stay.