Fabrication of nano filler doped PVA/starch biodegradable composites with enhanced thermal conduction, water barrier and antimicrobial performance for food industry.

In this work there was investigated the synergistic effect of the nanomaterials-the Montmorillonite (MMT) and the vanadium pentoxide (V2O5) on the polyvinyl alcohol (PVA)/starch composite. The composite films were prepared by the solvent casting method. The characterization of the composites showed that the addition of the MMT and the V2O5 to PVA/starch composite decreased the water solubility and water absorption capacity of the film. Both of the reinforcement materials enriched values of thermal conductivity and thermal stability of the composite. The TG/DTA and universal testing machine (UTM) analysis exhibited that MMT and V2O5 augmented the thermal robustness and tensile strength of composites and decreased the strain to break. It was also observed that greater MMT concentration accelerates mechanical strength deterioration of the film owing to agglomeration. The scanning electron microscopy (SEM) analysis reflected great change in the surface morphology of the films in the presence and absence of MMT and V2O5. This was due to the interaction amid constituents of the composite. The chemical interaction between the PVA, Starch, MMT and the V2O5 was also established via Fourier-transform infrared spectroscopy (FTIR) analysis, which revealed fluctuations in the absorbance position and intensity of the PVA/Starch. Antimicrobial activities against seven different cultures of bacteria (both-gram positive and -negative) and one fungus (Candida albicans), exposed that antimicrobial performance of the PVA amplified upon addition of the starch, MMT and V2O5, making these composites prospective candidates for the biodegradable packaging materials.

Improved estimation of population distribution function using twofold auxiliary information under simple random sampling.

In this article, our main aim is to suggest enhanced families of estimators for estimating the population distribution function (DF) using twofold auxiliary evidence within the framework of simple random sampling. Numerical analysis is performed on four different actual data sets. The precision of the estimators is further investigated exhausting a simulation study. As equated with existing estimators, the suggested families of estimators have minimum mean square error (MSE) and higher percentage relative efficiency (PRE). The succeeding recommended family of estimators outperforms the first family of estimators across all data sets. These are positive indicators of its performance. The theoretical result shows that the recommended family of estimators performs better than the existing estimators. The extent of improvement in efficiency is noteworthy, indicating the superiority of the suggested estimators in terms of minimum MSE.

The prevalence of peripheral neuropathy among the patients with diabetes in Pakistan: a systematic review and meta-analysis.

The most frequent complication of diabetes is peripheral neuropathy. The estimated prevalence of peripheral neuropathy in people with diabetes varies substantially between published studies in Pakistan. We conducted this meta-analysis to summarize the prevalence of peripheral neuropathy in people with diabetes. Different electronic databases were systematically searched using keywords and MeSH terms. Random-effects meta-analysis was conducted to pool the prevalence of peripheral neuropathy in people with diabetes in Pakistan. Heterogeneity was investigated by random-effects meta-regression and stratification. Two independent authors reviewed studies, extracted data, and conducted the risk of bias analysis. Nineteen studies with a total of 8487 diabetic patients were included. The overall pooled prevalence of diabetic peripheral neuropathy was 43.16% (95% CI 32.93-53.69%), with significant heterogeneity between estimates. The prevalence of peripheral neuropathy among those newly diagnosed with diabetes was 26.52% (95% CI 14.97-39.96%, n = 5). According to the subgroup meta-analysis, the pooled prevalence of diabetic peripheral neuropathy was highest in Khyber Pakhtunkhwa (55.29%; 95% CI 23.91-84.50%), followed by Sindh (40.04%; 95% CI 24.00-57.25%), and the lowest was found in Punjab (34.90%; 95% CI 15.05-57.95%). A significant association was found between the pooled prevalence estimate and the duration of diabetes. The results of this meta-analysis indicate a relatively high prevalence of peripheral neuropathy in people with diabetes in Pakistan. The study protocol has been registered in the PROSPERO, with the registration number CRD42022371617.

A simulation study: Using dual ancillary variable to estimate population mean under stratified random sampling.

In this paper, we propose an improved ratio-in-regression type estimator for the finite population mean under stratified random sampling, by using the ancillary varaible as well as rank of the ancillary varaible. Expressions of the bias and mean square error of the estimators are derived up to the first order of approximation. The present work focused on proper use of the ancillary variable, and it was discussed how ancillary variable can improve the precision of the estimates. Two real data sets as well as simulation study are carried out to observe the performances of the estimators. We demonstrate theoretically and numerically that proposed estimator performs well as compared to all existing estimators.

A simulation study: Improved ratio-in-regression type variance estimator based on dual use of auxiliary variable under simple random sampling.

In this article, we proposed an improved finite population variance estimator based on simple random sampling using dual auxiliary information. Mathematical expressions of the proposed and existing estimators are obtained up to the first order of approximation. Two real data sets are used to examine the performances of a new improved proposed estimator. A simulation study is also recognized to assess the robustness and generalizability of the proposed estimator. From the result of real data sets and simulation study, it is examining that the proposed estimator give minimum mean square error and percentage relative efficiency are higher than all existing counterparts, which shown the importance of new improved estimator. The theoretical and numerical result illustrated that the proposed variance estimator based on simple random sampling using dual auxiliary information has the best among all existing estimators.

Probabilistic Approach to COVID-19 Data Analysis and Forecasting Future Outbreaks Using a Multi-Layer Perceptron Neural Network.

The present outbreak of COVID-19 is a worldwide calamity for healthcare infrastructures. On a daily basis, a fresh batch of perplexing datasets on the numbers of positive and negative cases, individuals admitted to hospitals, mortality, hospital beds occupied, ventilation shortages, and so on is published. Infections have risen sharply in recent weeks, corresponding with the discovery of a new variant from South Africa (B.1.1.529 also known as Omicron). The early detection of dangerous situations and forecasting techniques is important to prevent the spread of disease and restart economic activities quickly and safely. In this paper, we used weekly mobility data to analyze the current situation in countries worldwide. A methodology for the statistical analysis of the current situation as well as for forecasting future outbreaks is presented in this paper in terms of deaths caused by COVID-19. Our method is evaluated with a multi-layer perceptron neural network (MLPNN), which is a deep learning model, to develop a predictive framework. Furthermore, the Case Fatality Ratio (CFR), Cronbach's alpha, and other metrics were computed to analyze the performance of the forecasting. The MLPNN is shown to have the best outcomes in forecasting the statistics for infected patients and deaths in selected regions. This research also provides an in-depth analysis of the emerging COVID-19 variants, challenges, and issues that must be addressed in order to prevent future outbreaks.

Modified estimators of finite population distribution function based on dual use of auxiliary information under stratified random sampling.

In survey sampling, information on auxiliary variables related to the main variable is often available in many practical problems. Since the mid-twentieth century, researchers have taken a keen interest in the use of auxiliary information due to its usefulness in estimation methods. The current study presents two new estimators for the distribution function of a finite population based on dual auxiliary variables. The new estimators can be used in situations where the researchers face some sort of complex data set. The mathematical equations for the bias and mean square error have been obtained for each proposed estimator. Besides, an empirical study simulation study has also been conducted to analyse the performance of estimators. It is found that the new suggested estimators of the distribution function of a finite population are more accurate than some of the existing estimators.

A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net.

In prostate cancer, fusion biopsy, which couples magnetic resonance imaging (MRI) with transrectal ultrasound (TRUS), poses the basis for targeted biopsy by allowing the comparison of information coming from both imaging modalities at the same time. Compared with the standard clinical procedure, it provides a less invasive option for the patients and increases the likelihood of sampling cancerous tissue regions for the subsequent pathology analyses. As a prerequisite to image fusion, segmentation must be achieved from both MRI and TRUS domains. The automatic contour delineation of the prostate gland from TRUS images is a challenging task due to several factors including unclear boundaries, speckle noise, and the variety of prostate anatomical shapes. Automatic methodologies, such as those based on deep learning, require a huge quantity of training data to achieve satisfactory results. In this paper, the authors propose a novel optimization formulation to find the best superellipse, a deformable model that can accurately represent the prostate shape. The advantage of the proposed approach is that it does not require extensive annotations, and can be used independently of the specific transducer employed during prostate biopsies. Moreover, in order to show the clinical applicability of the method, this study also presents a module for the automatic segmentation of the prostate gland from MRI, exploiting the nnU-Net framework. Lastly, segmented contours from both imaging domains are fused with a customized registration algorithm in order to create a tool that can help the physician to perform a targeted prostate biopsy by interacting with the graphical user interface.

A Computational approach to screen, predict and annotate human and chimpanzee PHEX intronic miRNAs, their gene targets, and regulatory interaction networks.

The knowledge of what separates us genetically from our less-evolved relatives is crucial for gaining new biomedical insight about the human-chimpanzee relatedness that could influence the development of new treatments and diagnostic aids for various ailments. Especially, more than 300 diseases have been mapped to the X chromosome, which has unique and complicated characteristics than other chromosomes in the human genome. Although the genomes of humans and chimpanzees share 99% similarity, significant differences exist between the two species in their non-coding intronic regions. Therefore, this evolutionary-based genome annotation study attempted to computationally compare, contrast, and annotate the homologous miRNAs and their gene regulatory mechanisms in the intronic regions of the PHEX gene on the human X chromosome of the two species. From our results, we identified a total of 1296 human miRNAs and 46, 957 gene targets. Similarly, 30, 563 targets of homologous chimp miRNAs were predicted. miRNAs like hsa-miR-17-5p showed a maximum number of interactions while miRNAs like hsa-miR-107 with the least number of interactions in the human/chimp gene networks. A few top-ranked miRNAs such as hsa-miR-24, hsa-miR-145, hsa-miR-34a, and hsa-miR-378 were observed to be common between the two genera. The cooperativity and multiplicity of certain miRNAs were predicted to regulate the expression of diverse cancer-associated genes such as Cyclin D1, Notch1, CDK-6, E2F3, ALK4, CKDN2A, DHFR, and MAPK14. Nevertheless, further in vitro and in vivo experimental validations of these gene candidates are required before they could be used as potential diagnostic markers and drug targets.

Green synthesis of silver nanoparticles from aqueous extract of Scutellaria barbata and coating on the cotton fabric for antimicrobial applications and wound healing activity in fibroblast cells (L929).

Scutellaria barbata is a perennial herb which was vastly prescribed in Chinese medicine to treat inflammations, infections and it is also used a detoxifying agent. We synthesized silver nanoparticles with Scutellaria barbata extract and characterized the nanoparticles with UV-Vis spectroscopic analysis, TEM, AFM, FTIR and XRD. The biofilm inhibiting property of synthesized silver nanoparticles were examined with XTT reduction assay and the antimicrobial property was examined with well diffusion method. The silver nanoparticles were also coated with cotton fabrics and their efficacy against antimicrobials was analyzed to prove its application. The cytotoxic property of synthesized silver nanoparticles was examined with L929 fibroblast cells using MTT assay. Finally we analyzed the wound healing property of synthesized silver nanoparticles with wound scratch assay. The result of our UV-Vis spectroscopic analysis confirms Scutellaria barbata aqueous extract reduced silver ions and synthesized silver nanoparticles. The characterization studies TEM, AFM, FTIR and XRD confirms the synthesized silver nanoparticles are in ideal shape and size to be utilized as a drug. The XTT reduction assay proves silver nanoparticles effectively inhibits the biofilm formation in both resistant and sensitive strains. Antimicrobial sensitivity tests confirms synthesized silver nanoparticles and cotton coated synthesized silver nanoparticles both are effective against gram positive, gram negative and fungal species. Further the results of MTT assay confirms the synthesized silver nanoparticles are non toxic and finally the wound healing potency of the nanoparticles was confirmed with wound scratch assay. Over all our results authentically confirms the silver nanoparticles synthesized with Scutellaria barbata aqueous extract is potent wound healing drug.

Phyllanthin inhibits MOLT-4 leukemic cancer cell growth and induces apoptosis through the inhibition of AKT and JNK signaling pathway.

Among cancers, leukemia is a multistep progression that involves genetic modifications of normal hematopoietic progenitor cells to cancerous cells. In recent times, leukemia cases and their mortality rate have increased rapidly. Therefore, the immense need for a therapeutic approach is crucial that can control this type of cancer. Phyllanthin is a lignan compound constituent from the Phyllanthus species and has numerous beneficial effects as a dietary component. The present study aims to determine the impact of phyllanthin on the MOLT-4 cytotoxic effect. MOLT-4 cells and MS-5 cells were cultured at different concentrations of phyllanthin (5, 10, 25, 50, 75, and 100 µM/ml), and the viability was assessed by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide method. The level of reactive oxygen species, the membrane potential of mitochondria, apoptosis by 2',7'-dichlorofluorescin-diacetate (DCF-DA), rhodamine, acridine orange (AO)/ethidium bromide (EB), 4',6-diamidino-2-phenylindole (DAPI)/propidium iodide (PI) staining, gene expression of signaling molecules, and protein levels were assessed by reverse-transcription polymerase chain reaction and western blot analysis. Phyllanthin did not show toxicity toward MS-5 cells and significantly decreased the cell viability of MOLT-4 cells with an IC50 value of 25 µM/ml. Also, phyllanthin induced the production of reactive oxygen species and led to the loss of mitochondrial membrane potential. AO/EB and DAPI/PI staining fluorescent image confirmed the induction of apoptosis by phyllanthin treatment. The messenger RNA (mRNA) expression of cell cycle regulator cyclin D1, antiapoptotic gene Bcl-2, NF-κB, and TNF-α decreased, but the proapoptotic Bax mRNA expression was increased. The phosphorylated protein levels of p-PI3K1/2, p-ERK1/2, and p-AKT were decreased, whereas the levels of p-p38 and p-JNKT1/2 increased. Our results confirmed that phyllanthin inhibits the MOLT-4 cells, increases apoptosis, and inhibits MOLT-4 migration and cell invasion. Therefore, phyllanthin can be used as a potential target for leukemia treatment.

Tomentosin Reduces Behavior Deficits and Neuroinflammatory Response in MPTP-Induced Parkinson's Disease in Mice.

Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide. Tomentosin is an active compound isolated from the I. viscosa plant that has extensive therapeutic value. In this exploration, the neuroprotective actions of tomentosin were investigated against MPTP-stimulated neuroinflammation in mice. PD was stimulated in C57/BL6 mice by injecting 20-mg/kg MPTP at 2-h intervals 4 times a day for 15 days simultaneously with tomentosin treatment. The rota-rod test, grasping test, and pole climbing test were executed to investigate the motor functioning of the test animals. Proinflammatory cytokines, reactive oxygen species, and myeloperoxidase were assayed using commercial ELISA kits. Superoxide dismutase enzyme levels were measured by the standard method. Expression of TLR-4/NF-κB was analyzed by Western blot. Brain tissues of investigational animals were analyzed microscopically. Tomentosin treatment of the MPTP-intoxicated PD mice promoted appreciable regains in body weight and noticeably prevented MPTP-stimulated impairments in motor function. In the PD mice, proinflammatory cytokine, ROS, and MPO levels were lowered by tomentosin, inhibited the TLR-4/NF-κB signaling pathway and prevented inflammation-mediated neuronal cell damage, and reduced glial cell damage and normalized ganglion layers. These findings confirmed the neuroprotective properties of tomentosin against MPTP-induced PD in mice.

Eugenol-piperine loaded polyhydroxy butyrate/polyethylene glycol nanocomposite-induced apoptosis and cell death in nasopharyngeal cancer (C666-1) cells through the inhibition of the PI3K/AKT/mTOR signaling pathway.

Nasopharyngeal cancer is a malignancy developing from the nasopharynx epithelium due to smoking and nitrosamine-containing foods. Nasopharyngeal cancer is highly endemic to Southeast Asia. Eugenol and piperine have shown many anticancer activities on numerous cancer types, like colon, lung, liver, and breast cancer. In this study, we amalgamated eugenol and piperine loaded with a polyhydroxy butyrate/polyethylene glycol nanocomposite (Eu-Pi/PHB-PEG-NC) for better anticancer results against nasopharyngeal cancer (C666-1) cells. In the current study, nasopharyngeal cancer cell lines C666-1 were utilized to appraise the cytotoxic potential of Eug-Pip-PEG-NC on cell propagation, programmed cell death, and relocation. Eu-Pi/PHB-PEG-NC inhibits cellular proliferation on C666-1 cells in a dose-dependent manner, and when compared with 20 µg/ml, 15 µg/ml of loaded mixture evidently restrained the passage aptitude of C666-1 cells, this was attended with a downregulated expression of mitochondrial membrane potential. Treatment with 15 µg/ml Eu-Pi/PHB-PEG-NC suggestively amplified cell apoptosis in the C666-1 cells. Furthermore, its cleaved caspase-3, 8, and 9 and Bax gene expression was augmented and Bcl-2 gene expression was diminished after Eu-Pi/PHB-PEG-NC treatment. Additionally, our data established that the collective effect of Eu-Pi/PHB-PEG-NC loaded micelles inhibited the expansion of C666-1 cells augmented apoptosis connected with the intrusion of PI3K/Akt/mTOR signaling pathway.

Osteogenic differentiation and mineralization potential of zinc oxide nanoparticles from Scutellaria baicalensis on human osteoblast-like MG-63 cells.

Development of biologically inspired green synthesis of silver nanoparticles has been extensively scrutinized owing to its uses in biomedical industry. In the last two decades, the demands of nanomaterial in bone remodelling have increased. Scutellaria baicalensis is a flowering plant usually used for many ailments. This work explores the zinc oxide nanoparticles (ZnO NPs) by green route method from S. baicalensis and the therapeutic potentials of Sb-ZnONPs on differentiation of osteoblast and osteoclast formation inhibition. The characterization of the fabricated ZnO-NPs from S. baicalensis was done via different spectroscopic and microscopic techniques; ultraviolet-visible spectroscopy (UV-Vis), Fourier transform-infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), and dynamic light scattering (DLS). The Osteogenic-related tests (MTT, Mineralization assay and Real-time PCR) were used to evaluate the properties of SB-ZnONPs on the growth and proliferation of human osteoblast-like MG-63 cells. The characterization of SB-ZnONPs discovered the crystalline properties with high zinc content and the existence of bioactive mixtures from S. baicalensis extract. In addition, SB-ZnONPs showed insignificant cytotoxicity with enhanced differentiation, proliferation, and mineralization on MG-63 cells. Overall, these results denote that SB-ZnONPs is expected to be a natural source for the development of medical agents to in bone healing and remodelling.

Estimation of finite population distribution function with dual use of auxiliary information under non-response.

In this paper, we propose two new families of estimators for estimating the finite population distribution function in the presence of non-response under simple random sampling. The proposed estimators require information on the sample distribution functions of the study and auxiliary variables, and additional information on either sample mean or ranks of the auxiliary variable. We considered two situations of non-response (i) non-response on both study and auxiliary variables, (ii) non-response occurs only on the study variable. The performance of the proposed estimators are compared with the existing estimators available in the literature, both theoretically and numerically. It is also observed that proposed estimators are more precise than the adapted distribution function estimators in terms of the percentage relative efficiency.

Finite population distribution function estimation with dual use of auxiliary information under simple and stratified random sampling.

The main purpose of this paper is to propose two new estimators for estimating the finite population distribution function under simple and stratified random sampling schemes using supplementary information on the distribution function, mean and ranks of the auxiliary variable. The mathematical expressions for the bias and mean squared error of the proposed estimators are derived under the first order of approximation. The theoretical and empirical studies showed that the proposed estimators uniformly perform better than the existing estimators in terms of the percentage relative efficiency.

Molecular docking analysis of doronine derivatives with human COX-2.

Cyclooxygenase-2 (COX-2) is linked to inflammation. Therefore, it is of interest to design and develop novel inhibitors for COX-2. Hence, we report the molecular docking based binding features of doronine derivatives (desacetyldoronine, floradnin, onetine, 22310115, 21159807) with the human Cyclooxygenase-2 as potential inhibitors. A pyrrolizidine alkaloid doronine a molecular constituents of Emilia sonchifolia is an herbal alternative to known drugs in the prophylaxis of inflammation. We report the molecular docking, pharmacophore, ADMET and molecular properties analysis data of doronine and its similar compounds. Docking and ADMET Data shows that COX-2 binds with doronine with optimal features for further consideration.

Statistical analysis of forecasting COVID-19 for upcoming month in Pakistan.

In this paper, we have conducted analysis based on data obtained from National Institute of Health (NIH) - Islamabad and produced a forecast of COVID-19 confirmed cases as well as the number of deaths and recoveries in Pakistan using the Auto-Regressive Integrated Moving Average Model (ARIMA). The fitted forecasting models revealed high exponential growth in the number of confirmed cases, deaths and recoveries in Pakistan. Based on our model prediction the number of confirmed cases will be increased by 2.7 times, 95% prediction interval for the number of cases at the end of May 2020 = (5681 to 33079). There could be up to 500 deaths, 95% prediction interval = (168 to 885) and there could be eightfold increase in the number of recoveries, 95% prediction interval = (2391 to 16126). The forecasting results of COVID-19 are alarming for May in Pakistan. The health officials and government should adopt new strategies to control the pandemic from further spread until a proper treatment or vaccine is developed.

Malrotation of the gut with tuberculous stricture.

Malrotation of the gut is a congenital anomaly of the gastrointestinal tract and usually present with obstruction in early childhood. Late presentation may come into light for associated pathology. We report a case that had developed obstruction due to tuberculous stricture and not due to the anomaly itself.