Pro- and anti-inflammatory cytokines are the game-changers in childhood obesity-associated metabolic disorders (diabetes and non-alcoholic fatty liver diseases).

Childhood obesity is a chronic inflammatory epidemic that affects children worldwide. Obesity affects approximately 1 in 5 children worldwide. Obesity in children can worsen weight gain and raise the risk of obesity-related comorbidities like diabetes and non-alcoholic fatty liver disease (NAFLD). It can also negatively impact the quality of life for these children. Obesity disrupts immune system function, influencing cytokine (interleukins) balance and expression levels, adipokines, and innate and adaptive immune cells. The altered expression of immune system mediators, including interleukin-1 (IL-1), interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-17 (IL-17), interleukin-18 (IL-18), transforming growth factor (TGF), tumor necrosis factor (TNF), and others, caused inflammation, progression, and the development of pediatric obesity and linked illnesses such as diabetes and NAFLD. Furthermore, anti-inflammatory cytokines, including interleukin-2 (IL-2), have been shown to have anti-diabetes and IL-1 receptor antagonist (IL-1Ra) anti-diabetic and pro-NAFLFD properties, and interleukin-10 (IL-10) has been shown to have a dual role in managing diabetes and anti-NAFLD. In light of the substantial increase in childhood obesity-associated disorders such as diabetes and NAFLD and the absence of an effective pharmaceutical intervention to inhibit immune modulation factors, it is critical to consider the alteration of immune system components as a preventive and therapeutic approach. Thus, the current review focuses on the most recent information regarding the influence of pro- and anti-inflammatory cytokines (interleukins) and their molecular mechanisms on pediatric obesity-associated disorders (diabetes and NAFLD). Furthermore, we discussed the current therapeutic clinical trials in childhood obesity-associated diseases, diabetes, and NAFLD.

Pro-inflammatory cytokines and CXC chemokines as game-changer in age-associated prostate cancer and ovarian cancer: Insights from preclinical and clinical studies' outcomes.

Prostate cancer (PC) and Ovarian cancer (OC) are two of the most common types of cancer that affect the reproductive systems of older men and women. These cancers are associated with a poor quality of life among the aged population. Therefore, finding new and innovative ways to detect, treat, and prevent these cancers in older patients is essential. Finding biomarkers for these malignancies will increase the chance of early detection and effective treatment, subsequently improving the survival rate. Studies have shown that the prevalence and health of some illnesses are linked to an impaired immune system. However, the age-associated changes in the immune system during malignancies such as PC and OC are poorly understood. Recent research has suggested that the excessive production of inflammatory immune mediators, such as interleukin-6 (IL-6), interleukin-8 (IL-8), transforming growth factor (TGF), tumor necrosis factor (TNF), CXC motif chemokine ligand 1 (CXCL1), CXC motif chemokine ligand 12 (CXCL12), and CXC motif chemokine ligand 13 (CXCL13), etc., significantly impact the development of PC and OC in elderly patients. Our review focuses on the latest functional studies of pro-inflammatory cytokines (interleukins) and CXC chemokines, which serve as biomarkers in elderly patients with PC and OC. Thus, we aim to shed light on how these biomarkers affect the development of PC and OC in elderly patients. We also examine the current status and future perspective of cytokines (interleukins) and CXC chemokines-based therapeutic targets in OC and PC treatment for elderly patients.

The role of proinflammatory cytokines and CXC chemokines (CXCL1-CXCL16) in the progression of prostate cancer: insights on their therapeutic management.

Reproductive cancers are malignancies that develop in the reproductive organs. One of the leading cancers affecting the male reproductive system on a global scale is prostate cancer (PCa). The negative consequences of PCa metastases endure and are severe, significantly affecting mortality and life quality for those who are affected. The association between inflammation and PCa has captured interest for a while. Inflammatory cells, cytokines, CXC chemokines, signaling pathways, and other elements make up the tumor microenvironment (TME), which is characterized by inflammation. Inflammatory cytokines and CXC chemokines are especially crucial for PCa development and prognosis. Cytokines (interleukins) and CXC chemokines such as IL-1, IL-6, IL-7, IL-17, TGF-ß, TNF-α, CXCL1-CXCL6, and CXCL8-CXCL16 are thought to be responsible for the pleiotropic effects of PCa, which include inflammation, progression, angiogenesis, leukocyte infiltration in advanced PCa, and therapeutic resistance. The inflammatory cytokine and CXC chemokines systems are also promising candidates for PCa suppression and immunotherapy. Therefore, the purpose of this work is to provide insight on how the spectra of inflammatory cytokines and CXC chemokines evolve as PCa develops and spreads. We also discussed recent developments in our awareness of the diverse molecular signaling pathways of these circulating cytokines and CXC chemokines, as well as their associated receptors, which may one day serve as PCa-targeted therapies. Moreover, the current status and potential of theranostic PCa therapies based on cytokines, CXC chemokines, and CXC receptors (CXCRs) are examined.

Synthesis of Silver Nanoparticles from Ganoderma Species and Their Activity against Multi Drug Resistant Pathogens.

The widespread and indiscriminate use of broad-spectrum antibiotics leads to microbial resistance, which causes major problems in the treatment of infectious diseases. However, advances in nanotechnology using mushrooms have opened up new domains for the synthesis and use of nanoparticles against multidrug-resistant pathogens. Mushooms have recently attracted attention and are exploited for food and medicinal purposes. The current study focuses on the molecular identification, characterization of biologically synthesized silver nanoparticles by X-ray diffraction (XRD) spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), UV-Vis spectroscopy and scanning electron microscopy (SEM) and antibacterial analysis of extract and silver nanoparticles (AgNPs) synthesis from Ganoderma resinaceum against multidrug resistant microbes. Accurate identification of mushrooms is key in utilizing them for the benefit of humans. However, morphological identification of mushrooms is time consuming, tedious and may be prone to error. Molecular techniques are quick and reliable tools that are useful in mushroom taxonomy. Blast results showed that G. resinaceum (GU451247) obtained from Pakistan was 97 % same to the recognized G. resinaceum (GU451247) obtained from China as well as G. resinaceum (GU451247) obtained from India. The antimicrobial potential of mushroom composite and AgNPs showed high efficacy against pathogenic Staphylococcus aureus (ZOI 23 mm) K. pneumonia (ZOI 20 mm), Pseudomonas aeruginosa (ZOI 24 mm) and E. fecalis and A. baumannii (ZOI 10 mm), and multidrug resistant (MDR) A. baumannii (ZOI 24 mm). XRD evaluation revealed the crystalline composition of synthesized NPs with diameter of 45 nm. UV-Vis spectroscopy obsorption peaked of 589 nm confirmed the presence of AgNPs. SEM results showed the cubic morphology of AgNPs. The FTIR analysis of NPs obtained from G. resinaceum containing C=O as well as (O=C-H) stretching revealed presence of hydrogen, carbonyl and amide groups. The synthesized extract and AgNPs showed promising minimum inhibitory concentration (MIC) at 2 mg concentration against the MDR strains. AgNPs are observed to be efficient as they need less quantities to prevent bacterial growth. In the view of challenges for developing antimicrobial NPs of variable shape and size by various other methods, tuning nanoparticles synthesized via mushrooms can be a wonderful approach to resolve existing hurdles.

The association between dietary patterns before pregnancy and gestational diabetes mellitus: a matched case-control study in China.

BACKGROUND AND OBJECTIVES: We aimed to explore the relationship between dietary patterns and gestational diabetes mellitus (GDM) during pre-pregnancy six months using principal component analysis (PCA) and the geometric framework for nutrition (GFN). METHODS AND STUDY DESIGN: We conducted a case-control study that included 210 GDM pregnant women and 210 controls. The dietary intake of all participants was assessed by a validated semi-quantitative food frequency questionnaire (FFQ). Major dietary patterns were extracted by PCA. A conditional logistic regression model was used to determine whether specific dietary patterns are associated with the risk of GDM. Meanwhile, the relationship between dietary patterns and GDM was visualized using GFN. RESULTS: Four major dietary patterns were identified: "protein-rich pattern," "plant-based pattern," "oil-pickles-desserts pattern," and "cereals-nuts pattern." After adjustment for confounders, the "plant-based pattern" was associated with decreased risk of GDM (Q4 vs. Q1: OR = 0.01, 95% CI: 0.00-0.08), whereas no significant association was found in other dietary patterns. Moreover, there was no dietary intake of ice cream cones and deep-fried dough sticks for the population, which would produce fewer patients with GDM. Deep-fried dough sticks had statistically significant differences in the case and control groups (p < 0.001), while ice cream cones had the opposite result. CONCLUSIONS: The "plant-based pattern" may reduce the risk of GDM. Besides, although the "cereals-nuts pattern" had no association with GDM risk, avoiding the intake of deep-fried dough sticks could decrease GDM risk.

Glycyrrhizin ameliorates impaired glucose metabolism and ovarian dysfunction in a polycystic ovary syndrome mouse model.

The aim of this study was to determine the impact of glycyrrhizin, an inhibitor of high mobility group box 1, on glucose metabolic disorders and ovarian dysfunction in mice with polycystic ovary syndrome. We generated a polycystic ovary syndrome mouse model by using dehydroepiandrosterone plus high-fat diet. Glycyrrhizin (100 mg/kg) was intraperitoneally injected into the polycystic ovary syndrome mice and the effects on body weight, glucose tolerance, insulin sensitivity, estrous cycle, hormone profiles, ovarian pathology, glucolipid metabolism, and some molecular mechanisms were investigated. Increased number of cystic follicles, hormonal disorders, impaired glucose tolerance, and decreased insulin sensitivity in the polycystic ovary syndrome mice were reverted by glycyrrhizin. The increased high mobility group box 1 levels in the serum and ovarian tissues of the polycystic ovary syndrome mice were also reduced by glycyrrhizin. Furthermore, increased expressions of toll-like receptor 9, myeloid differentiation factor 88, and nuclear factor kappa B as well as reduced expressions of insulin receptor, phosphorylated protein kinase B, and glucose transporter type 4 were restored by glycyrrhizin in the polycystic ovary syndrome mice. Glycyrrhizin could suppress the polycystic ovary syndrome-induced upregulation of high mobility group box 1, several inflammatory marker genes, and the toll-like receptor 9/myeloid differentiation factor 88/nuclear factor kappa B pathways, while inhibiting the insulin receptor/phosphorylated protein kinase B/glucose transporter type 4 pathways. Hence, glycyrrhizin is a promising therapeutic agent against polycystic ovary syndrome.

Thiourea derivatives inhibit key diabetes-associated enzymes and advanced glycation end-product formation as a treatment for diabetes mellitus.

This study was designed to screen novel thiourea derivatives against different enzymes, such as α-amylase, α-glucosidase, protein tyrosine phosphatase 1 B, and advanced glycated end product (AGEs). A cytotoxicity analysis was performed using rat L6 myotubes and molecular docking analysis was performed to map the binding interactions between the active compounds and α-amylase and α-glucosidase. The data revealed the potency of five compounds, including E (1-(2,4-difluorophenyl)-3-(3,4-dimethyl phenyl) thiourea), AG (1-(2-methoxy-5-(trifluoromethyl) phenyl)-3-(3-methoxy phenyl) thiourea), AF (1-(2,4-dichlorophenyl)-3-(4-ethylphenyl) thiourea), AD (1-(2,4-dichlorophenyl)-3-(4-ethylphenyl) thiourea), and AH (1-(2,4-difluorophenyl)-3-(2-iodophenyl) thiourea), showed activity against α-amylase. The corresponding percentage inhibitions were found to be 85 ± 1.9, 82 ± 0.7, 75 ± 1.2, 72 ± 0.4, and 65 ± 1.1%, respectively. These compounds were then screened using in vitro assays. Among them, AH showed the highest activity against α-glucosidase, AGEs, and PTP1B, with percentage inhibitions of 86 ± 0.4% (IC50  = 47.9 µM), 85 ± 0.7% (IC50  = 49.51 µM), and 85 ± 0.5% (IC50  = 79.74 µM), respectively. Compound AH showed an increased glucose uptake at a concentration of 100 µM. Finally, an in vivo study was conducted using a streptozotocin-induced diabetic mouse model and PTP1B expression was assessed using real-time PCR. Additionally, we examined the hypoglycemic effect of compound AH in diabetic rats compared to the standard drug glibenclamide.

A narrative review: CXC chemokines influence immune surveillance in obesity and obesity-related diseases: Type 2 diabetes and nonalcoholic fatty liver disease.

Adipose tissue develops lipids, aberrant adipokines, chemokines, and pro-inflammatory cytokines as a consequence of the low-grade systemic inflammation that characterizes obesity. This low-grade systemic inflammation can lead to insulin resistance (IR) and metabolic complications, such as type 2 diabetes (T2D) and nonalcoholic fatty liver disease (NAFLD). Although the CXC chemokines consists of numerous regulators of inflammation, cellular function, and cellular migration, it is still unknown that how CXC chemokines and chemokine receptors contribute to the development of metabolic diseases (such as T2D and NAFLD) during obesity. In light of recent research, the objective of this review is to provide an update on the linkage between the CXC chemokine, obesity, and obesity-related metabolic diseases (T2D and NAFLD). We explore the differential migratory and immunomodulatory potential of CXC chemokines and their mechanisms of action to better understand their role in clinical and laboratory contexts. Besides that, because CXC chemokine profiling is strongly linked to leukocyte recruitment, macrophage recruitment, and immunomodulatory potential, we hypothesize that it could be used to predict the therapeutic potential for obesity and obesity-related diseases (T2D and NAFLD).

Association of diet quality with the risk of Sarcopenia based on the Chinese diet balance index 2016: a cross-sectional study among Chinese adults in Henan Province.

BACKGROUND: Sarcopenia can lead to a series of unfavourable health outcomes. Diet is an important factor influencing sarcopenia. In this study, we aimed to evaluate the association of sarcopenia with diet quality assessed by the Chinese Diet Balance Index 2016 (DBI-16). METHODS: A cross-sectional study was conducted to collect information on nutrition and health in Henan Province, China, and a total of 644 individuals were studied. Sarcopenia was defined according to the Asian Working Group for Sarcopenia (AWGS) criteria updated in 2019. Diet quality was assessed by using the Chinese Diet Balance Index 2016 (DBI-16), which includes three indicators: the lower bound score (LBS), higher bound score (HBS) and diet quality distance (DQD). Binary logistic regression analysis was used to estimate the risk of sarcopenia associated with diet quality. RESULTS: A total of 49 of the 644 participants were diagnosed with sarcopenia. Excessive intake (score > 0) of cereals, meat, eggs and salt, inadequate intake (score < 0) of vegetables, fruits, dairy products, soybeans and low diet variety were commonly seen in both groups of participants. The participants with sarcopenia had a more serious inadequate intake of fruit than those without sarcopenia (p < 0.05). The overall LBS, HBS and DQD in both groups were in the interval of low-level problems. Compared with participants with a suitable LBS, those with an unsuitable LBS were more likely to have a low gait speed (OR: 2.58; 95%CI: 1.13-7.04) after multiple adjustments. However, the other two DBI-16 indicators, the HBS and DQD, were not associated with sarcopenia or its related diagnostic variables. CONCLUSION: Unfavourable diet quality, mainly referring to inadequate dietary intake in this study, may be a risk factor for low gait speed.

Exploring the Therapeutic Properties of Alga-Based Silver Nanoparticles: Anticancer, Antibacterial, and Free Radical Scavenging Capabilities.

The current study was designed to evaluate the antioxidant, anticancer and antimicrobial activities of silver nanoparticles (AgNPs) biosynthesized by Spirulina platensis extract. The biosynthesized silver nanoparticles were characterized using Fourier transform infrared (FT-IR) analysis, scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray diffraction (XRD) analysis. The antioxidant activity of the biosynthesized AgNPs were determined via DPPH radical scavenging assay while its anticancer activity was determined using the MTT assay. The antimicrobial activity of the biosynthesized AgNPs were analyzed by disc diffusion method. Spirulina platensis acts as a reducing and capping agent. The efficacy of silver nanoparticles (AgNPs) in inhibiting the growth of Gram-negative bacteria, specifically Acetobacter, Klebsiella, Proteus vulgaris, and Pseudomonas aeruginosa, was assessed by the utilisation of the diffusion method. The study aimed to evaluate the efficacy of biosynthesized silver nanoparticles (AgNPs) against many strains of Pseudomonas aeruginosa bacteria. The findings of the study revealed that when administered in doses of 50 µl, 75 µl, and 100 µl, the largest observed zone of inhibition corresponded to measurements of 10.5 mm, 14 mm, and 16 mm, respectively. A zone of inhibition with dimensions of 8 mm, 10.5 mm, and 12 mm was detected during testing against Acetobacter at concentrations of 50 µl, 75 µl, and 100 µl, respectively. The findings also indicate that there is a positive correlation between the concentration of AgNP and the DPPH scavenging ability of silver nanoparticles. The percentage of inhibition observed at concentrations of 500 µg/ml, 400 µg/ml, 300 µg/ml, 200 µg/ml, and 100 µg/ml were recorded as 80±1.98, 61±1.98, 52±1.5, 42±1.99, and 36±1.97, respectively. In addition, it was observed that the silver nanoparticles exhibited the greatest antioxidant activity at a concentration of 500 g/ml, with a measured value of 80.89±1.99. The IC-50 values, representing the inhibitory concentration required to achieve 50 % inhibition, were found to be 8.16, 19.15, 30.14, 41.13, and 63.11 at inhibition levels of 36±1.97, 42±1.99, 52±1.5, 61±1.98, and 80±1.98, respectively.

Enhanced Solubility and Stability of Aripiprazole in Binary and Ternary Inclusion Complexes Using Hydroxy Propyl Beta Cyclodextrin (HPßCD) and L-Arginine.

The low water solubility of an active pharmaceutical ingredient (aripiprazole) is one of the most critical challenges in pharmaceutical research and development. This antipsychotic drug has an inadequate therapeutic impact because of its minimal and idiosyncratic oral bioavailability to treat schizophrenia. The main objective of this study was to improve the solubility and stability of the antipsychotic drug aripiprazole (ARP) via forming binary as well as ternary inclusion complexes with hydroxypropyl-ß-cyclodextrin (HPßCD) and L-Arginine (LA) as solubility enhancers. Physical mixing and lyophilization were used in different molar ratios. The developed formulations were analyzed by saturation solubility analysis, and dissolution studies were performed using the pedal method. The formulations were characterized by FTIR, XRD, DSC, SEM, and TGA. The results showcased that the addition of HPßCD and LA inclusion complexes enhanced the stability, in contrast to the binary formulations and ternary formulations prepared by physical mixing and solvent evaporation. Ternary formulation HLY47 improved dissolution rates by six times in simulated gastric fluid (SGF). However, the effect of LA on the solubility enhancement was concentration-dependent and showed optimal enhancement at the ratio of 1:1:0.27. FTIR spectra showed the bond shifting, which confirmed the formation of new complexes. The surface morphology of complexes in SEM studies showed the rough surface of lyophilization and solvent evaporation products, while physical mixing revealed a comparatively crystalline surface. The exothermic peaks in DSC diffractograms showed diminished peaks previously observed in the diffractogram of pure drug and LA. Lyophilized ternary complexes displayed significantly enhanced thermal stability, as observed from the thermograms of TGA. In conclusion, it was observed that the preparation method and a specific drug-to-polymer and amino acid ratio are critical for achieving high drug solubility and stability. These complexes seem to be promising candidates for novel drug delivery systems development.

Molecular Profiling, Characterization and Antimicrobial Efficacy of Silver Nanoparticles Synthesized from Calvatia gigantea and Mycena leaiana against Multidrug-Resistant Pathogens.

The use of natural products isolated from mushrooms against infection, cancer diseases and other oxidative-stress-related diseases is one of the cornerstones of modern medicine. Therefore, we tried to establish a combination of medicinal mushrooms and nanotechnology possibly with the field of medicine for the development of antibacterial agents against these MDR strains. The aim of the research was to understand the molecular identification, characterization and antibacterial action of Calvatia gigantea and Mycena leaiana. The identification of fruiting body species via morpho-anatomical and molecular methods was necessary to analyze the genetic variability and phylogenetic relationships of mushrooms. Phylogenetic analysis revealed that Calvatia from Hunza, Pakistan, exhibited 98% resemblance to the previously discovered Langermannia gigantean (DQ112623) and L. gigantean (LN714562) from northern Europe, and Mycena (Pakistan) showed a 97% similarity to M. leaiana (MF686520) and M. leaiana (MW448623) from the USA. UV-vis, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) were used for AgNPs' characterization. The UV-vis absorption peak of 500-600 nm indicates the AgNPs' presence. XRD results determined Calvatia gigantea AgNPs were nanocrystals and Mycena leaiana seems to be amorphous. In addition, SEM results showed the cubic morphology of C. gigantea with a diameter of 65 nm, and the FTIR spectra of fruiting body revealed the presence of functional groups-carboxyl, nitro, and hydroxyl-in AgNPs, which catalyzed the reduction of Ag+ to Ag0. Further antibacterial activity of mushrooms against MDR strains was determined via agar well diffusion assay, and Minimum Inhibitory Concentration (MIC) was estimated by qualitative experimentation using the broth dilution method. All experiments were conducted in triplicate. The results showed that the mushroom AgNPs, along with their synergy and nano-composites (with the exception of Ethyl-acetate), were shown to have zones of inhibition from 4 mm to 29 mm against multidrug-resistant pathogens such as Acinetobacter baumannii, Staphylococcus aureus, Pseudomonas aeruginosa, Klebsiella pneumonia, Proteus mirabilis, Enterobacter cloacae and Escherichia coli. The mushroom composites were active against most of the tested microorganisms whilst the lowest MIC value (10-40 mg/mL) was recorded against MDR strains. Hence, the present study suggested the possibility of employing compounds present in mushrooms for the development of new antibacterial agents, as well as efflux pump inhibitors.

Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda.

The COVID19 pandemic has impacted the global economy, social activities, and Electricity Consumption (EC), affecting the performance of historical data-based Electricity Load Forecasting (ELF) algorithms. This study thoroughly analyses the pandemic's impact on these models and develop a hybrid model with better prediction accuracy using COVID19 data. Existing datasets are reviewed, and their limited generalization potential for the COVID19 period is highlighted. A dataset of 96 residential customers, comprising 36 and six months before and after the pandemic, is collected, posing significant challenges for current models. The proposed model employs convolutional layers for feature extraction, gated recurrent nets for temporal feature learning, and a self-attention module for feature selection, leading to better generalization for predicting EC patterns. Our proposed model outperforms existing models, as demonstrated by a detailed ablation study using our dataset. For instance, it achieves an average reduction of 0.56% & 3.46% in MSE, 1.5% & 5.07% in RMSE, and 11.81% & 13.19% in MAPE over the pre- and post-pandemic data, respectively. However, further research is required to address the varied nature of the data. These findings have significant implications for improving ELF algorithms during pandemics and other significant events that disrupt historical data patterns.

Phycoremediation of industrial wastewater using Vaucheria debaryana and Cladophora glomerata.

Rapid urbanization and industrialization are regarded as the leading causes of environmental pollution, mainly aquatic pollution. This study was carried out to investigate the use of algal species Cladophora glomerata (CG) and Vaucheria debaryana (VD) as a cost-effective and environmentally friendly phycoremediators for composite industrial effluent. After the pot experimentation using algal species, a considerable decrease in electrical conductivity (EC: 49.10-81.46%), dissolved oxygen (DO: 3.76-8.60%), biological oxygen demand (BOD: 7.81-39.28%), chemical oxygen demand (COD: 7.81-39.28%), total suspended solids (TSS: 38.09-62.21%), and total dissolved solids (TDS: 38.09-62.21%) was observed. Before and after experimentation, the heavy metals were also quantified using atomic absorption spectrophotometry (AAS), and considerable reduction was observed in Cd (41.02-48.75%) and Pb (48.72-57.03%) concentrations. The Cd concentration determined in CTCG (control treatment for Cladophora glomerata containing tap water), CG (treatment pot for Cladophora glomerata containing industrial effluents), CTVD (control pot for Vaucheria debaryana containing tap water), and VD (treatment pot for Vaucheria debaryana containing industrial effluents) biomass was 0.06, 0.499, 0.035, and 0.476 mg/kg, respectively. The Pb uptake determined in CTCG, CG, CTVD, and VD was 0.32, 1.12, 0.31, and 0.49 mg/kg, respectively, using wet digestion method and ASS. The data revealed that C. glomerata has the highest bioconcentration factor for Cd (98.42%), followed by Pb (92.57%) in treatment pots containing industrial effluents (CG and VD). Furthermore, C. glomerata showed the highest bioconcentration factor for Pb (86.49%) as compared to Cd (75%) in tap water (CTCG and CTVD). The t test analysis revealed that heavy metal concentrations significantly (p ≤ 0.05) reduced through the phycoremediation process. The analysis found that C. glomerata removed 48.75% of Cd and 57.027% of Pb from industrial effluents. Phytotoxicity assay was also performed by cultivating Triticum sp. in order to analyze the toxicity of the untreated (control) and treated water samples. Phytotoxicity result shows that the effluent treated with both Cladophora glomerata and Vaucheria debaryana gives better wheat (Triticum sp.) plant % germination, plant height (cm), and root height (cm). The highest plant % germination was showed by treated CTCG (90%), followed by CTVD (80%) and CG (70%) and VD (70%). The study concluded that phycoremediation using C. glomerata and V. debaryana is one of the environment-friendly approaches. The proposed algal-based strategy is economically viable and environmentally sustainable that can be utilized for the remediation of industrial effluents.

Sustained virological response to antiviral drugs in treatment of different genotypes of HCV cirrhotic patients.

Cirrhosis and liver cancer are both caused by hepatitis C virus (HCV) infection of the liver. Patients with HCV cirrhosis may be treated with one of many antiviral medications, depending on their specific genotype. Samples of cirrhotic HCV were obtained from 190 people at the Khyber Teaching Hospital and the Hayatabad Medical Complex in Peshawar, Pakistan. Multiplex and real-time PCR were used to assess the genotypes and viral loads of the samples, respectively. Sixty patients were given sofosbuvir plus daclatasvir with ribavirin, while the remaining 56 patients were given sofosbuvir with ribavirin for a period of 12-24 weeks. LFTs were also tracked both before and after therapy. Group I (sofosbuvir + daclatasvir) had a sustained virological response of 82.70 percent. Group II (sofosbuvir + daclatasvir with ribavirin) had an 86% sustained virological response, whereas group III (84% sustained virological response) received only ribavirin. When compared to other genotypes, genotype 3 showed the most impressive sustained virologic response (SVR) to the antiviral medicines. Based on the results of this trial, we propose sofosbuvir + daclatasvir ribavirin for the treatment of cirrhotic patients with various HCV genotypes since it produces the greatest sustained virological response.

A decrease in cluster of differentiation 2 expression on natural killer cells is associated with polycystic ovary syndrome but not influenced by metformin in a mouse model†.

PROBLEM: Natural killer (NK) cells from the peripheral blood and spleen represent the source from which various tissues replenish their immune cell populations. Hyperandrogenism and high interleukin-2 (IL-2) levels are factors present in polycystic ovary syndrome (PCOS). These factors and metformin, one of the commonest medications used in treating PCOS, may have an impact on NK cells. However, this is presently unknown. Here, we aimed to assess the distribution of peripheral blood and splenic NK cells and their CD2 and CD94 expression patterns in a PCOS mouse model and test whether metformin could reverse these effects. METHOD OF STUDY: Four mouse groups were designed as follows (n = 15/group): control, PCOS, PCOS plus vehicle, PCOS plus metformin. Dehydroepiandrosterone and a high-fat diet were administered to induce the PCOS mouse model. Flow cytometry was used to analyze the expressions of CD2 and CD94 on peripheral blood and splenic NK cells. RESULTS: PCOS mice had a low surface-density of CD2 on peripheral blood NK cells and a decreased percentage of CD2+ splenic NK cells. Metformin administration did not significantly influence these changes; however, it reduced the splenic NK cell counts. CONCLUSIONS: Our findings proved the association of PCOS with an altered expression of CD2 on peripheral blood and splenic NK cells and that of metformin with a lowered splenic NK cell reserve in PCOS conditions. These findings could further unlock key mechanisms in PCOS pathophysiology and in the mechanism of action of metformin, towards improving PCOS management.

Ovarian inflammatory mRNA profiles of a dehydroepiandrosterone plus high-fat diet-induced polycystic ovary syndrome mouse model.

RESEARCH QUESTION: What is the expression pattern of inflammatory mRNA profiles of a dehydroepiandrosterone (DHEA) plus high-fat diet (HFD)-induced polycystic ovary syndrome (PCOS) mouse model? DESIGN: RNA sequencing was performed to investigate the mRNA expression profiles in the ovarian tissues of a DHEA plus HFD-induced PCOS mouse model. Six samples were divided into two groups (control and PCOS), with three biological replicates in each group. This was followed by hierarchical clustering, gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. The relative expression levels of nine inflammatory genes were validated via quantitative reverse-transcription polymerase chain reaction. RESULTS: A total of 436 genes were differentially expressed between the control and PCOS mice. Out of these, 137 genes were up-regulated while 299 genes were down-regulated. Gene ontology analysis indicated that differentially expressed mRNA were associated with T cell-mediated cytotoxicity and homocysteine metabolic processes. Pathway analysis further showed that these abnormally expressed mRNA were associated with signalling pathways, such as NF-kB signalling, tyrosine metabolism and phenylalanine metabolism. All these pathways are involved in chronic inflammation and PCOS. CONCLUSION: The differentially expressed genes are potentially involved in the inflammation that is evident in PCOS, and so could serve as therapeutic options against the disease. Nevertheless, prospective studies are needed to test this hypothesis.

A Deep Sequence Learning Framework for Action Recognition in Small-Scale Depth Video Dataset.

Depth video sequence-based deep models for recognizing human actions are scarce compared to RGB and skeleton video sequences-based models. This scarcity limits the research advancements based on depth data, as training deep models with small-scale data is challenging. In this work, we propose a sequence classification deep model using depth video data for scenarios when the video data are limited. Unlike summarizing the frame contents of each frame into a single class, our method can directly classify a depth video, i.e., a sequence of depth frames. Firstly, the proposed system transforms an input depth video into three sequences of multi-view temporal motion frames. Together with the three temporal motion sequences, the input depth frame sequence offers a four-stream representation of the input depth action video. Next, the DenseNet121 architecture is employed along with ImageNet pre-trained weights to extract the discriminating frame-level action features of depth and temporal motion frames. The extracted four sets of feature vectors about frames of four streams are fed into four bi-directional (BLSTM) networks. The temporal features are further analyzed through multi-head self-attention (MHSA) to capture multi-view sequence correlations. Finally, the concatenated genre of their outputs is processed through dense layers to classify the input depth video. The experimental results on two small-scale benchmark depth datasets, MSRAction3D and DHA, demonstrate that the proposed framework is efficacious even for insufficient training samples and superior to the existing depth data-based action recognition methods.

An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos.

Video anomaly recognition in smart cities is an important computer vision task that plays a vital role in smart surveillance and public safety but is challenging due to its diverse, complex, and infrequent occurrence in real-time surveillance environments. Various deep learning models use significant amounts of training data without generalization abilities and with huge time complexity. To overcome these problems, in the current work, we present an efficient light-weight convolutional neural network (CNN)-based anomaly recognition framework that is functional in a surveillance environment with reduced time complexity. We extract spatial CNN features from a series of video frames and feed them to the proposed residual attention-based long short-term memory (LSTM) network, which can precisely recognize anomalous activity in surveillance videos. The representative CNN features with the residual blocks concept in LSTM for sequence learning prove to be effective for anomaly detection and recognition, validating our model's effective usage in smart cities video surveillance. Extensive experiments on the real-world benchmark UCF-Crime dataset validate the effectiveness of the proposed model within complex surveillance environments and demonstrate that our proposed model outperforms state-of-the-art models with a 1.77%, 0.76%, and 8.62% increase in accuracy on the UCF-Crime, UMN and Avenue datasets, respectively.

A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal.

Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model's classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models' effectiveness.