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Nanotechnology has been actively integrated as drug carriers over the last few years to treat various cancers. The main hurdle in the clinical management of cancer is the development of multidrug resistance against chemotherapeutic agents. To overcome the limitations of chemotherapy, the researchers have been developing technological advances for significant progress in the oncotherapy by enabling the delivery of chemotherapeutic agents at increased drug content levels to the targeted spots. Several nano-drug delivery systems designed for tumor-targeting are evaluated in preclinical and clinical trials and showed promising outcomes in cancerous tumors' clinical management. This review describes nanocarrier's importance in managing different types of cancers and emphasizing nanocarriers for drug delivery and cancer nanotherapeutics. It also highlights the recent advances in nanocarriers-based delivery systems, including polymeric nanocarriers, micelles, nanotubes, dendrimers, magnetic nanoparticles, solid lipid nanoparticles, and quantum dots (QDs). The nanocarrier-based composites are discussed in terms of their structure, characteristics, and therapeutic applications in oncology. To conclude, the challenges and future exploration opportunities of nanocarriers in chemotherapeutics are also presented.

Phyto-synthesized nanoparticles (NPs) having reduced chemical toxicity have been focused globally and become essential component of nanotechnology recently. We prepared green phytochemically (ginger and garlic) reduced NiO-NPs to replace synthetic bactericidal and catalytic agent in textile industry. NPs were characterized using ultra-violet visible spectroscopy (UV-Vis), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Fourier-transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy (EDS), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The synthesis of NPs was confirmed by XRD and UV-Vis having strong absorption at 350 nm with size ranged between 16–52 nm for ginger and 11–59 nm for garlic. Scanning and transmission electron microscopy confirmed pleomorphism with cubic- and more spherical-shaped NPs. Moreover, exact quantities of garlic and ginger extracts (1:3.6 ml) incorporated to synthesize NiO-NPs have been successfully confirmed by FTIR. Phytochemically reduced NPs by garlic presented enhanced bactericidal activity against multiple drug-resistant Staphylococcus aureus at increasing concentrations (0.5, 1.0 mg/50 μl) and also degraded methylene blue (MB) dye efficiently. Conclusively, green synthesized NiO-NPs are impending activists to resolve drug resistance as well as environment friendly catalytic agent that may be opted at industrial scale.

Human Action Recognition (HAR) is a popular area of research in computer vision due to its wide range of applications such as surveillance, health care, and gaming, etc. Action recognition based on 3D skeleton data allows simplistic, cost-efficient models to be formed making it a widely used method. In this work, we propose DSwarm-Net , a framework that employs deep learning and swarm intelligence-based metaheuristic for HAR that uses 3D skeleton data for action classification. We extract four different types of features from the skeletal data namely: Distance, Distance Velocity, Angle, and Angle Velocity, which capture complementary information from the skeleton joints for encoding them into images. Encoding the skeleton data features into images is an alternative to the traditional video-processing approach and it helps in making the classification task less complex. The Distance and Distance Velocity encoded images have been stacked depth-wise and fed into a Convolutional Neural Network model which is a modified version of Inception-ResNet. Similarly, the Angle and Angle Velocity encoded images have been stacked depth-wise and fed into the same network. After training these models, deep features have been extracted from the pre-final layer of the networks, and the obtained feature representation is optimized by a nature-inspired metaheuristic, called Ant Lion Optimizer, to eliminate the non-informative or misleading features and to reduce the dimensionality of the feature set. DSwarm-Net has been evaluated on three publicly available HAR datasets, namely UTD-MHAD, HDM05, and NTU RGB+D 60 achieving competitive results, thus confirming the superiority of the proposed model compared to state-of-the-art models.

As the risk of diseases diabetes and hypertension increases, machine learning algorithms are being utilized to improve early stage diagnosis. This study proposes a Hybrid Prediction Model (HPM), which can provide early prediction of type 2 diabetes (T2D) and hypertension based on input risk-factors from individuals. The proposed HPM consists of Density-based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection to remove the outlier data, Synthetic Minority Over-Sampling Technique (SMOTE) to balance the distribution of class, and Random Forest (RF) to classify the diseases. Three benchmark datasets were utilized to predict the risk of diabetes and hypertension at the initial stage. The result showed that by integrating DBSCAN-based outlier detection, SMOTE, and RF, diabetes and hypertension could be successfully predicted. The proposed HPM provided the best performance result as compared to other models for predicting diabetes as well as hypertension. Furthermore, our study has demonstrated that the proposed HPM can be applied in real cases in the IoT-based Health-care Monitoring System, so that the input risk-factors from end-user android application can be stored and analyzed in a secure remote server. The prediction result from the proposed HPM can be accessed by users through an Android application; thus, it is expected to provide an effective way to find the risk of diabetes and hypertension at the initial stage.

Globally, cervical cancer remains as the foremost prevailing cancer in females. Hence, it is necessary to distinguish the importance of risk factors of cervical cancer to classify potential patients. The present work proposes a cervical cancer prediction model (CCPM) that offers early prediction of cervical cancer using risk factors as inputs. The CCPM first removes outliers by using outlier detection methods such as density-based spatial clustering of applications with noise (DBSCAN) and isolation forest (iForest) and by increasing the number of cases in the dataset in a balanced way, for example, through synthetic minority over-sampling technique (SMOTE) and SMOTE with Tomek link (SMOTETomek). Finally, it employs random forest (RF) as a classifier. Thus, CCPM lies on four scenarios: (1) DBSCAN + SMOTETomek + RF, (2) DBSCAN + SMOTE+ RF, (3) iForest + SMOTETomek + RF, and (4) iForest + SMOTE + RF. A dataset of 858 potential patients was used to validate the performance of the proposed method. We found that combinations of iForest with SMOTE and iForest with SMOTETomek provided better performances than those of DBSCAN with SMOTE and DBSCAN with SMOTETomek. We also observed that RF performed the best among several popular machine learning classifiers. Furthermore, the proposed CCPM showed better accuracy than previously proposed methods for forecasting cervical cancer. In addition, a mobile application that can collect cervical cancer risk factors data and provides results from CCPM is developed for instant and proper action at the initial stage of cervical cancer.

Houttuynia cordata Thunb (H. cordata) is a rhizomatous, herbaceous, and perennial plant widely distributed in Asia. It has multiple chemical constituents, such as alkaloids, essential oils, phenolic acids, and flavonoids used against various health problems. The essential oils and flavonoids are the main components of H. cordata that play an essential role in disease treatment and traditional health care. Moreover, the leaves and stems of H. cordata have a long medicinal history in China. In addition, H. cordata is used against several health issues, such as cold, cough, fever, pneumonia, mumps, and tumors, due to its anti-inflammatory, anti-bacterial, anti-viral, anti-oxidant, and anti-tumor effects. It protects organs due to its anti-inflammatory activity. H. cordata regulates immunity by enhancing immune barriers of the oral cavity, vagina, and gastrointestinal tract, and shows broad-spectrum activity against liver, lung, breast, and colon tumors. However, there are some gaps to be filled to understand its pathways and mechanisms. Mechanisms such as its interaction with cells, cell membranes, and various drugs are important. Studies in relation to the blood–brain barrier, lipophilicity, cAMP signaling, and skin permeability, including pharmaceutical effects, will be very useful. This review includes the biological and pharmacological activities of H. cordata based on up-to-date research.

Due to climate change, temperature in late February and early March raised up which cause heat stress at reproductive stage (terminal growth phase of wheat crop) which has become the major causative factor towards low wheat production in arid and semiarid regions. Therefore; strategies need to be adopted for improving terminal heat stress tolerance in wheat. In this study, we assessed whether foliar application of silicon (Si) (2 and 4 mM) at terminal growth phase i.e. heading stage of wheat imposed to heat stress (37 ± 2 °C) under polythene tunnel could improve the performance of wheat. Results of the study revealed that heat stress significantly reduced the photosynthetic pigments (chlorophyll a, b and a + b and carotenoids) leading to a lower grain yield. However, a 4 mM Si application (foliar applied) at heading stage prominently increased the chlorophyll a, b and a + b and carotenoids of flag leaf by improving the activities of enzymatic antioxidants (catalase, peroxidase and superoxide dismutase) and osmoprotectants (soluble sugar protein and proline) under terminal heat stress. Improvements in the performance of wheat (chlorophyll contents, carotenoids, soluble sugar and proteins and proline and yield) with foliar application of Si were also observed under control conditions. Correlation analysis revealed strong association ( r  > 0.90) of chlorophyll contents and carotenoids with grain and biological yield. Negative correlation (−0.81 <  r  > −0.63) of physio-biochemical components (antioxidants, proline, soluble sugars and proteins) with yield revealed that under heat stress these components produced in more quantities to alleviate the effects of heat, and Si application also improved these physio biochemical components. In crux, foliar application of Si alleviates the losses in the performance of wheat caused by terminal heat stress by improving the antioxidant mechanism and production of osmoprotectants.

The main goal of the current paper is to contribute to the existing literature of probability distributions. In this paper, a new probability distribution is generated by using the Alpha Power Family of distributions with the aim to model the data with non-monotonic failure rates and provides a better fit. The proposed distribution is called Alpha Power Exponentiated Inverse Rayleigh or in short APEIR distribution. Various statistical properties have been investigated including they are the order statistics, moments, residual life function, mean waiting time, quantiles, entropy, and stress-strength parameter. To estimate the parameters of the proposed distribution, the maximum likelihood method is employed. It has been proved theoretically that the proposed distribution provides a better fit to the data with monotonic as well as non-monotonic hazard rate shapes. Moreover, two real data sets are used to evaluate the significance and flexibility of the proposed distribution as compared to other probability distributions.

Reducing greenhouse gas (GHG) emissions is a global concern after Paris Agreement (PA). Identification of GHG emission sources and accurate and precise estimation of the corresponding emissions is the first step to meet reduction targets under PA. Increasing share of agricultural emissions in the global concentration has raised concerns on this sector. Now, reducing agricultural emissions without compromising food security is a real challenge. The present study was aimed to provide the current emission profile of Pakistan’s agriculture, historical emission trends and future projections under agricultural growth scenarios according to prescribed guidelines of Intergovernmental Panel on Climate Change (IPCC) for national GHGs inventory development. In this study, GHG emissions were estimated using United Nations Framework Convention on Climate Change (UNFCCC) Non-Annex-I Inventory Software (NAIIS), version 1.3.2 as per prescribed Revised 1996 IPCC Guidelines. In these emission estimations, tier-1 approach (which employs default emission factors) was used in accordance with national circumstances and data availability in the country. The emissions baseline was projected for 2030 under business as usual (BAU), food security (FS) and enhanced consumption pattern (ECP) scenarios. Agriculture sector emitted 174.6 million tons (Mt) of carbon dioxide equivalent (CO 2 -equivalent) emissions, of which 89.8 Mt is methane (CH 4 ) and 83.7 Mt is nitrous oxide (N 2 O). Carbon monoxide (CO) emissions were found to be 1.07 Mt of CO 2 -equivalent. Emission from agricultural soils constituted 45.5% of the total agricultural emissions followed by 45.1% from enteric fermentation and 6.5% from livestock manure management. The rest of 1.7% of the emissions were from rice cultivation followed by 1.1% from burning of crop residue. Historical emission trends showed that the agricultural emissions grew from 71.6 to 174.6 Mt of CO 2 -equivalent from 1994 to 2015, a 143.8% increase over the period of 21 years. Emissions baseline projections were found to be 271.9, 314.3 and 362.9 Mt tons of CO 2 -equivalent under BAU, FS and ECP scenarios, respectively.

Heat stress along with low water availability at reproductive stage (terminal growth phase of wheat crop) is major contributing factor towards less wheat production in tropics and sub-tropics. Flag leaf plays a pivotal role in assimilate partitioning and stress tolerance of wheat during terminal growth phase. However, limited is known about biochemical response of flag leaf to combined and individual heat and drought stress during terminal growth phase. Therefore, current study investigated combined and individual effect of terminal drought and heat stress on water relations, photosynthetic pigments, osmolytes accumulation and antioxidants defense mechanism in flag leaf of bread wheat. Experimental treatments comprised of control, terminal drought stress alone (50% field capacity during reproductive phase), terminal heat stress alone (wheat grown inside plastic tunnel during reproductive phase) and terminal drought stress + terminal heat stress. Individual and combined imposition of drought and heat stresses significantly (p≤0.05) altered water relations, osmolyte contents, soluble proteins and sugars along with activated antioxidant defensive system in terms of superoxide dismutase (SOD), peroxidase (POD) and ascorbate peroxidase (APX). Turgor potential, POD and APX activities were lowest under individual heat stress; however, these were improved when drought stress was combined with heat stress. It is concluded that combined effect of drought and heat stress was more detrimental than individual stresses. The interactive effect of both stresses was hypo-additive in nature, but for some traits (like turgor potential and APX) effect of one stress neutralized the other. To best of our knowledge, this is the first report on physiological and biochemical response of flag leaf of wheat to combine heat and drought stress. These results will help future studies dealing with improved stress tolerance in wheat. However, detailed studies are needed to fully understand the genetic mechanisms behind these physiological and biochemical changes in flag leaf in response to combined heat and drought stress.