Clinico-genomic findings, molecular docking, and mutational spectrum in an understudied population with breast cancer patients from KP, Pakistan

In this study, we report the mutational profiles, pathogenicity, and their association with different clinicopathologic and sociogenetic factors in patients with Pashtun ethnicity for the first time. A total of 19 FFPE blocks of invasive ductal carcinoma (IDC) from the Breast Cancer (BC) tissue and 6 normal FFPE blocks were analyzed by whole-exome sequencing (WES). Various somatic and germline mutations were identified in cancer-related genes, i.e., ATM, CHEK2, PALB2, and XRCC2. Among a total of 18 mutations, 14 mutations were somatic and 4 were germline. The ATM gene exhibited the maximum number of mutations (11/18), followed by CHEK2 (3/18), PALB2 (3/18), and XRCC2 (1/18). Except one frameshift deletion, all other 17 mutations were nonsynonymous single-nucleotide variants (SNVs). SIFT prediction revealed 7/18 (38.8%) mutations as deleterious. PolyPhen-2 and MutationTaster identified 5/18 (27.7%) mutations as probably damaging and 10/18 (55.5%) mutations as disease-causing, respectively. Mutations like PALB2 p.Q559R (6/19; 31.5%), XRCC2 p.R188H (5/19; 26.31%), and ATM p.D1853N (4/19; 21.05%) were recurrent mutations and proposed to have a biomarker potential. The protein network prediction was performed using GeneMANIA and STRING. ISPRED-SEQ indicated three interaction site mutations which were further used for molecular dynamic simulation. An average increase in the radius of gyration was observed in all three mutated proteins revealing their perturbed folding behavior. Obtained SNVs were further correlated with various parameters related to the clinicopathological status of the tumors. Three mutation positions (ATM p. D1853N, CHEK2 p.M314I, and PALB2 p.T1029S) were found to be highly conserved. Finally, the wild- and mutant-type proteins were screened for two drugs: elagolix (DrugBank ID: DB11979) and LTS0102038 (a triterpenoid, isolated from the anticancer medicinal plant Fagonia indica). Comparatively, a higher number of interactions were noted for normal ATM with both compounds, as compared to mutants.

An in silico approach to identify potential downstream targets of miR-153 involved in Alzheimer's disease

Background: In recent years, microRNAs (miRNAs) have emerged as key players in the pathophysiology of multiple diseases including Alzheimer's disease (AD). Messenger RNA (mRNA) targeting for regulation of gene expression by miRNAs has been implicated in the annotation of disease pathophysiology as well as in the explication of their starring role in contemporary therapeutic interventions. One such miRNA is miR-153 which mediates the survival of cortical neurons and inhibits plaque formation. However, the core mRNA targets of miR-153 have not been fully illustrated. Objective: The present study aimed to elucidate the potential involvement of miR-153 in AD pathogenesis and to reveal its downstream targets. Methods: miRanda was used to identify AD-associated targets of miR-153. TargetScan, PicTar, miRmap, and miRDB were further used to validate these targets. STRING 12 was employed to assess the protein-protein interaction network while Gene ontology (GO) analysis was carried out to identify the molecular functions exhibited by these gene targets. Results: In silico analysis using miRanda predicted five important AD-related targets of miR-153, including APP, SORL1, PICALM, USF1, and PSEN1. All five target genes are negatively regulated by miR-153 and are substantially involved in AD pathogenesis. A protein interaction network using STRING 12 uncovered 30 potential interacting partners for SORL1, PICALM, and USF1. GO analysis revealed that miR-153 target genes play a critical role in neuronal survival, differentiation, exon guidance, amyloid precursor protein processing, and synapse formation. Conclusion: These findings unravel the potential role of miR-153 in the pathogenesis of AD and provide the basis for forthcoming experimental studies.

Complex Materials with Stochastic Structural Patterns: Spiky Colloids with Enhanced Charge Storage Capacity

Self-assembled materials with complex nanoscale and mesoscale architecture attract considerable attention in energy and sustainability technologies. Their high performance can be attributed to high surface area, quantum effects, and hierarchical organization. Delineation of these contributions is, however, difficult because complex materials display stochastic structural patterns combining both order and disorder, which is difficult to be consistently reproduced yet being important for materials' functionality. Their compositional variability make systematic studies even harder. Here, a model system of FeSe2 "hedgehog" particles (HPs) was selected  to gain insight into the mechanisms of charge storage n complex nanostructured materials common for batteries and supercapacitors. Specifically, HPs represent self-assembled biomimetic nanomaterials with a medium level of complexity; they display an organizational pattern of spiky colloids with considerable disorder yet non-random; this patternt is consistently reproduced from particle to particle. . It was found that HPs can accommodate ≈70× greater charge density than spheroidal nano- and microparticles. Besides expanded surface area, the enhanced charge storage capacity was enabled by improved hole transport and reversible atomic conformations of FeSe2 layers in the blade-like spikes associated with the rotatory motion of the Se atoms around Fe center. The dispersibility of HPs also enables their easy integration into energy storage devices. HPs quadruple stored electrochemical energy and double the storage modulus of structural supercapacitors.

Exploring the relationship between SARS-CoV-2 variants, illness severity at presentation, in-hospital mortality and COVID-19 vaccination in a low middle-income country: A retrospective cross-sectional study

Background and Aims

COVID-19 morbidity and mortality varied globally through the pandemic. We studied the relationship of SARS-CoV-2 variants of concern (VOC) with COVID-19 severity and mortality among hospitalized patients in Pakistan.

Methods

A retrospective review of clinical, laboratory, and vaccination data of 197 COVID-19 adult patients at the Aga Khan University Hospital, Karachi between April 2021, and February 2022 was performed. SARS-CoV-2 VOC identified in respiratory samples were analyzed. Univariate and multivariate analysis was conducted to identify factors associated with COVID-19 outcomes.

Results

The median age of cases was 55 years and 51.8% were males. Twenty-four percent of females were pregnant. Of COVID-19 cases, 48.2% had nonsevere disease, while 52.8% had severe/critical disease. Hypertension (48%) and diabetes mellitus (41%) were common comorbids. SARS-CoV-2 VOC identified comprised; Omicron (55.3%), Beta (14.7%), Alpha (13.7%), Delta (12.7%), and Gamma (3.6%) variants. Most (59.7%) study subjects were unvaccinated. Of vaccines, 88% had received inactivated virus COVID-19 vaccines. Increased risk of severe disease was associated with age ≥50 years (odds ratio [OR]: 5.73; 95% confidence interval [CI]: [2.45-13.7]), as well as with diabetes mellitus (OR: 4.24; 95% CI: [1.82-9.85]). Full vaccination (OR: 0.25; 95% CI: [0.11-0.58]) or infection with Omicron (OR: 0.42; 95% CI: [0.23-0.74]) was associated with reduced disease severity. The risk of mortality increased with age ≥50 years (OR: 5.07; 95% CI: [1.92-13.42]) and a history of myocardial infarction (OR: 5.11; 95% CI: [1.45-17.93]) whilst, infection with Omicron was found to reduce the risk (OR: 0.22; 95% CI: [0.10-0.53]).

Conclusion

Our study describes the relationship between the severity of COVID-19, in-hospital mortality in relation to SARS-CoV-2 variants, and the impact of COVID-19 vaccination in Pakistan. Outcomes were more favorable in younger individuals, after vaccinations and with Omicron variant infections. Most cases received inactivated virus vaccines therefore these data highlight the protection provided against severe COVID-19.

Development of a tetravalent subunit vaccine against dengue virus through a vaccinomics approach

Dengue virus infection (DVI) is a mosquito-borne disease that can lead to serious morbidity and mortality. Dengue fever (DF) is a major public health concern that affects approximately 3.9 billion people each year globally. However, there is no vaccine or drug available to deal with DVI. Dengue virus consists of four distinct serotypes (DENV1-4), each raising a different immunological response. In the present study, we designed a tetravalent subunit multi-epitope vaccine, targeting proteins including the structural protein envelope domain III (EDIII), precursor membrane proteins (prM), and a non-structural protein (NS1) from each serotype by employing an immunoinformatic approach. Only conserved sequences obtained through a multiple sequence alignment were used for epitope mapping to ensure efficacy against all serotypes. The epitopes were shortlisted based on an IC50 value <50, antigenicity, allergenicity, and a toxicity analysis. In the final vaccine construct, overall, 11 B-cell epitopes, 10 HTL epitopes, and 10 CTL epitopes from EDIII, prM, and NS1 proteins targeting all serotypes were selected and joined via KK, AAY, and GGGS linkers, respectively. We incorporated a 45-amino-acid-long B-defensins adjuvant in the final vaccine construct for a better immunogenic response. The vaccine construct has an antigenic score of 0.79 via VaxiJen and is non-toxic and non-allergenic. Our refined vaccine structure has a Ramachandran score of 96.4%. The vaccine has shown stable interaction with TLR3, which has been validated by 50 ns of molecular dynamics (MD) simulation. Our findings propose that a designed multi-epitope vaccine has substantial potential to elicit a strong immune response against all dengue serotypes without causing any adverse effects. Furthermore, the proposed vaccine can be experimentally validated as a probable vaccine, suggesting it may serve as an effective preventative measure against dengue virus infection.

Towards deep learning based smart farming for intelligent weeds management in crops

Introduction

Deep learning (DL) is a core constituent for building an object detection system and provides a variety of algorithms to be used in a variety of applications. In agriculture, weed management is one of the major concerns, weed detection systems could be of great help to improve production. In this work, we have proposed a DL-based weed detection model that can efficiently be used for effective weed management in crops.

Methods

Our proposed model uses Convolutional Neural Network based object detection system You Only Look Once (YOLO) for training and prediction. The collected dataset contains RGB images of four different weed species named Grass, Creeping Thistle, Bindweed, and California poppy. This dataset is manipulated by applying LAB (Lightness A and B) and HSV (Hue, Saturation, Value) image transformation techniques and then trained on four YOLO models (v3, v3-tiny, v4, v4-tiny).

Results and discussion

The effects of image transformation are analyzed, and it is deduced that the model performance is not much affected by this transformation. Inferencing results obtained by making a comparison of correctly predicted weeds are quite promising, among all models implemented in this work, the YOLOv4 model has achieved the highest accuracy. It has correctly predicted 98.88% weeds with an average loss of 1.8 and 73.1% mean average precision value.

Future work

In the future, we plan to integrate this model in a variable rate sprayer for precise weed management in real time.

Weed infestation and productivity of wheat crop sown in various cropping systems under conventional and conservation tillage

Introduction

Climate change, pest infestation, and soil degradation are significantly reducing wheat (Triticum aestivum L.) yield. Wheat is cultivated in rice-wheat and cotton-wheat cropping systems and escalating global population is exerting substantial pressure on the efficiency of these systems. Conservation tillage and crop rotation could help in lowering soil degradation and pest infestation, and improving wheat yield.

Methods

This three-year study evaluated soil properties, weed infestation and wheat yield under various tillage and cropping systems. Six different cropping systems, i.e., cotton-wheat, sorghum-wheat, mungbean-wheat, rice-wheat, sunflower-wheat, and fallow-wheat (control) and three tillage systems, i.e., conventional tillage (CT), zero-tillage (ZT) and minimum tillage (MT) were included in the study.

Results

The individual and interactive effects of tillage and cropping systems significantly affected soil properties, weed infestation and yield of wheat crop. Overall, CT resulted in lower soil bulk density and higher porosity, while ZT behaved oppositely at all locations in this regard. Similarly, mungbean-wheat cropping system resulted in lower bulk density and higher porosity and nitrogen (N) contents, while fallow-wheat cropping system resulted in higher bulk density, and lower soil porosity and N contents. Similarly, ZT and CT resulted in higher and lower weed infestation, respectively. Likewise, lower and higher weed density and biomass were recorded in wheat-sorghum and wheat-fallow cropping systems, respectively at all locations. In the same way higher number of productive tillers, number of grains per spike, 1000-grain weight, grain yield, and economic returns of wheat crop were recorded for CT, whereas ZT resulted in lower values of these traits. Regarding interactions, wheat-mungbean cropping system with CT resulted in lower bulk density and higher porosity and N contents, whereas wheat-fallow system with ZT behaved oppositely at all locations in this regard. Similarly, higher and lower values for yield-related traits and economic returns of wheat crop were noted for mungbean-wheat cropping system under CT and fallow-wheat and sorghum-wheat cropping systems under ZT, respectively. It is concluded that the mungbean-wheat cropping system improved wheat productivity and soil health and sorghum-wheat cropping system could lower weed infestation. Therefore, these cropping systems can be practiced to lower weed infestation and improve wheat yield and economic returns.

A mobile-based system for maize plant leaf disease detection and classification using deep learning

Artificial Intelligence has been used for many applications such as medical, communication, object detection, and object tracking. Maize crop, which is the major crop in the world, is affected by several types of diseases which lower its yield and affect the quality. This paper focuses on this issue and provides an application for the detection and classification of diseases in maize crop using deep learning models. In addition to this, the developed application also returns the segmented images of affected leaves and thus enables us to track the disease spots on each leaf. For this purpose, a dataset of three maize crop diseases named Blight, Sugarcane Mosaic virus, and Leaf Spot is collected from the University Research Farm Koont, PMAS-AAUR at different growth stages on contrasting weather conditions. This data was used for training different prediction models including YOLOv3-tiny, YOLOv4, YOLOv5s, YOLOv7s, and YOLOv8n and the reported prediction accuracy was 69.40%, 97.50%, 88.23%, 93.30%, and 99.04% respectively. Results demonstrate that the prediction accuracy of the YOLOv8n model is higher than the other applied models. This model has shown excellent results while localizing the affected area of the leaf accurately with a higher confidence score. YOLOv8n is the latest model used for the detection of diseases as compared to the other approaches in the available literature. Also, worked on sugarcane mosaic virus using deep learning models has also been reported for the first time. Further, the models with high accuracy have been embedded in a mobile application to provide a real-time disease detection facility for end users within a few seconds.

Advancement in artificial intelligence for on-farm fruit sorting and transportation

On-farm sorting and transportation of postharvest fruit include sorting out defective products, grading them into categories based on quality, distributing them into bins, and carrying bins to field collecting stations. Advances in artificial intelligence (AI) can speed up on-farm sorting and transportation with high accuracy and robustness and significantly reduce postharvest losses. The primary objective of this literature review is to provide an overview to present a critical analysis and identify the challenges and opportunities of AI applications for on-farm sorting and transportation, with a focus on fruit. The challenges of on-farm sorting and transportation were discussed to specify the role of AI. Sensors and techniques for data acquisition were investigated to illustrate the tasks that AI models have addressed for on-farm sorting and transportation. AI models proposed in previous studies were compared to investigate the adequate approaches for on-farm sorting and transportation. Finally, the advantages and limitations of utilizing AI have been discussed, and in-depth analysis has been provided to identify future research directions. We anticipate that this survey will pave the way for further studies on the implementation of automated systems for on-farm fruit sorting and transportation.

Transgenerational impact of climatic changes on cotton production

Changing climatic conditions are an increasing threat to cotton production worldwide. There is a need to develop multiple stress-tolerant cotton germplasms that can adapt to a wide range of environments. For this purpose, 30 cotton genotypes were evaluated for two years under drought (D), heat (H), and drought + heat stresses (DH) under field conditions. Results indicated that plant height, number of bolls, boll weight, seed cotton yield, fiber fineness, fiber strength, fiber length, K⁺, K⁺/Na⁺, relative water contents (RWC), chlorophyll a and b, carotenoids, and total soluble proteins got reduced under D and H and were lowest under DH, whereas superoxidase dismutase (SOD), H₂O₂, Na⁺, GOT%, total phenolic contents, ascorbate, and flavonoids got increased for consecutive years. Correlation studies indicated that there was a positive correlation between most of the traits, but a negative correlation with H₂O₂ and Na⁺ ions. PCA and clustering analysis indicated that MNH-786, KAHKSHAN, CEMB-33, MS-71, FH-142, NIAB-820, CRS-2007, and FH-312 consistently performed better than other genotypes for most traits under stress conditions. Identified genotypes can be utilized in the future cotton breeding program to develop high-yielding, climate change-resilient cotton.

Combined effect of Zinc lysine and biochar on growth and physiology of wheat (Triticum aestivum L.) to alleviate salinity stress

Globally, Food security main threaten by abiotic stress like salinity and levels amongst the majority serious environmental stressors which reduce crop yield mass production. Biochar application has received much attention in agricultural practices as it enhances crop quality and production. The present study was carried out to analyze the role of lysine zinc and biochar on growth enhancement of wheat (Triticum aestivum L. cv. PU-2011) under saline stress (EC 7.17 dSm^-1). Seeds were sown in pots containing saline soil with and without 2% biochar, and foliar application of Zn-lysine (0, 1.0, and 2.0 mM) was made at different time intervals during plant growth. A combined application of biochar and Zn-lysine 2.0 mM highly improved the physiological attributes such as chlorophyll a (37%), chlorophyll b (60%), total chlorophyll (37%), carotenoids (16%), photosynthesis rate (Pn) 45%, stomatal conductance (gs) 53%, transpiration rate (Tr) 56%, and water use efficiency (WUE) 55%. The levels of malondialdehyde (MDA) 38%, hydrogen peroxide (H₂O₂) 62%, and electrolyte leakage (EL) 48% were decreased with the combined application of biochar and Zn-lysine 2.0 mM as compared with other treatments. The activities of catalase (CAT) 67%, superoxide dismutase (SOD) 70%, and ascorbate peroxidase (APX) 61% as well as catalase (CAT) 67% were regulated with the combined biochar and Zn-lysine 2.0 mM treatment. Similarly, the combined application of biochar and zinc-lysine (2.0 mM) enhanced the growth and yield attributes such as shoot length (79%), root fresh weight (62%), shoot fresh weight (36%), root dry weight (86%), shoot dry weight (39%), grain weight (57%), and spike length (43%) as compared with untreated control. The concentrations of sodium (Na) decreased whereas potassium (K), iron (Fe), and zinc (Zn) concentrations were enhanced in plants with the combined application of Zn-lysine and biochar. Overall, results showed that the combined application of Zn-lysine (2.0 mM) and biochar significantly inhibited the negative effect of salinity and improved the growth and physiological performance of wheat plants. The combined use of Zn-lysine and biochar might be a practical solution to tackle salt stress in plants, but field studies by growing various crops under varied environmental conditions are needed before any recommendation to farmers.

Medical terminology-based computing system: a lightweight post-processing solution for out-of-vocabulary multi-word terms

The linguistic rules of medical terminology assist in gaining acquaintance with rare/complex clinical and biomedical terms. The medical language follows a Greek and Latin-inspired nomenclature. This nomenclature aids the stakeholders in simplifying the medical terms and gaining semantic familiarity. However, natural language processing models misrepresent rare and complex biomedical words. In this study, we present MedTCS—a lightweight, post-processing module—to simplify hybridized or compound terms into regular words using medical nomenclature. MedTCS enabled the word-based embedding models to achieve 100% coverage and enabled the BiowordVec model to achieve high correlation scores (0.641 and 0.603 in UMNSRS similarity and relatedness datasets, respectively) that significantly surpass the n-gram and sub-word approaches of FastText and BERT. In the downstream task of named entity recognition (NER), MedTCS enabled the latest clinical embedding model of FastText-OA-All-300d to improve the F1-score from 0.45 to 0.80 on the BC5CDR corpus and from 0.59 to 0.81 on the NCBI-Disease corpus, respectively. Similarly, in the drug indication classification task, our model was able to increase the coverage by 9% and the F1-score by 1%. Our results indicate that incorporating a medical terminology-based module provides distinctive contextual clues to enhance vocabulary as a post-processing step on pre-trained embeddings. We demonstrate that the proposed module enables the word embedding models to generate vectors of out-of-vocabulary words effectively. We expect that our study can be a stepping stone for the use of biomedical knowledge-driven resources in NLP.

Response to a crisis and applicant attraction: Signaling employer brand personality and organizational trust through warm and competent COVID-19 responses

This paper investigates how organizations' response to a crisis such as the COVID-19 pandemic affects their employer attractiveness. Based on signaling theory, we argue that a COVID-19 response can signal an organization's employer brand personality, positively affecting applicant attraction. We conducted two experimental studies with employed and unemployed UK participants through Prolific Academic. Both studies indicate that a warm COVID-19 response leads to the highest employer attractiveness and job pursuit intentions, although a competent response was still more attractive than no response. Moreover, applicants use the warm and competent responses as signals of organizational warmth and competence respectively, building higher organizational trust. Limited support for the moderating role of applicants' personality was found. Implications during and beyond COVID-19 are discussed.

CRISPR/Cas9-Mediated Targeting of Susceptibility Factor eIF4E-Enhanced Resistance Against Potato Virus Y

Potato (Solanum tuberosum L.) is an important staple food around the world, and potato virus Y (PVY) is a major constraint for potato production. The VPg protein of PVY interacts with the translation initiation factor eIF4E of the host that works as a susceptibility factor during infection. The interaction between eIF4E and VPg was disrupted by CRISPR/Cas9. The homozygous conserved region of eIF4E of the potato variety “Kruda” was mutated by CRISPR/Cas9. Tracking of insertion, deletion, and conversion events was performed by Sanger sequencing with ∼15% editing efficiency. Truncated and mutated eIF4E proteins were unable to interact with VPg, and the virus was not able to exploit the host machinery for replication and systemic spreading. Mutated eIF4E lines showed enhanced resistance to PVYO strain. DAS-ELISA and RT-PCR were used for validation of the observed resistance. PVY resistance in tetraploid lines via CRISPR/Cas9 provides a route to develop novel resistant potato cultivars.

In Vitro, Ex Vivo, and In Vivo Evaluation of Nanoparticle-Based Topical Formulation Against Candida albicans Infection

Ketoconazole is commonly used in the treatment of topical fungal infections. The therapy requires frequent application for several weeks. Systemic side effects, allergic reactions, and prolonged treatment are often associated with non-compliance and therapy failure. Hence, we developed an optimized topical antifungal gel that can prolong the release of drug, reduce systemic absorption, enhance its therapeutic effect, and improve patient compliance. Ketoconazole-loaded PLGA nanoparticles were prepared by the emulsion/solvent evaporation method and were characterized with respect to colloidal properties, surface morphology, and drug entrapment efficiency. The optimized ketoconazole-loaded PLGA nanoparticles and commercially available silver nanoparticles were incorporated into a Carbopol 934P-NF gel base. This arrangement was characterized and compared with commercially available 2% ketoconazole cream to assess physical characteristics of the gel, in vitro drug release, ex vivo skin permeation and retention, and in vivo studies on Wister male albino rats. The results showed that polymeric PLGA nanoparticles were very effective in extending the release of ketoconazole in our optimized formulation. Nanoparticles were smooth, spherical in shape, and below 200 nm in size which is consistent with the data obtained from light scattering and SEM images. The ex vivo data showed that our gel formulation could strongly reduce drug permeation through the skin, and more than 60% of the drug was retained on the upper surface of the skin in contrast to 38.42% of the commercial cream. The in vivo studies showed that gel formulation could effectively treat the infection. This study demonstrates that our topical gel could be effective in sustaining the release of drug and suggests its potential use as a possible strategy to combat antifungal-resistant Candida albicans.

Mycovirus Hunting Revealed the Presence of Diverse Viruses in a Single Isolate of the Phytopathogenic Fungus Diplodia seriata From Pakistan

Diplodia seriata in the family Botryosphaeriaceae is a cosmopolitan phytopathogenic fungus and is responsible for causing cankers, fruit rot and leaf spots on economically important plants. In this study, we characterized the virome of a single Pakistani strain (L3) of D. seriata. Several viral-like contig sequences were obtained via a previously conducted next-generation sequencing analysis. Multiple infection of the L3 strain by eight RNA mycoviruses was confirmed through RT-PCR using total RNA samples extracted from this strain; the entire genomes were determined via Sanger sequencing of RT-PCR and RACE clones. A BLAST search and phylogenetic analyses indicated that these eight mycoviruses belong to seven different viral families. Four identified mycoviruses belong to double-stranded RNA viral families, including Polymycoviridae, Chrysoviridae, Totiviridae and Partitiviridae, and the remaining four identified mycoviruses belong to single-stranded RNA viral families, i.e., Botourmiaviridae, and two previously proposed families “Ambiguiviridae” and “Splipalmiviridae”. Of the eight, five mycoviruses appear to represent new virus species. A morphological comparison of L3 and partially cured strain L3ht1 suggested that one or more of the three viruses belonging to Polymycoviridae, “Splipalmiviridae” and “Ambiguiviridae” are involved in the irregular colony phenotype of L3. To our knowledge, this is the first report of diverse virome characterization from D. seriata.

Design and Characterization of Paclitaxel-Loaded Polymeric Nanoparticles Decorated With Trastuzumab for the Effective Treatment of Breast Cancer

The aim of the study was to design and formulate an antibody-mediated targeted, biodegradable polymeric drug delivery system releasing drug in a controlled manner to achieve a therapeutic goal for the effective treatment of breast cancer. Antibody-mediated paclitaxel-loaded PLGA polymeric nanoformulations were prepared by the solvent evaporation method using different experimental parameters and compatibility studies. The optimized formulations were selected for in vitro and in vivo evaluation and cytotoxicity studies. The in vitro drug release studies show a biphasic release pattern for the paclitaxel-loaded PLGA nanoparticles showing a burst release for 24 h followed by an extended release for 14 days; however, a more controlled and sustained release was observed for antibody-conjugated polymeric nanoparticles. The cytotoxicity of reference drug and paclitaxel-loaded PLGA nanoparticles with and without antibody was determined by performing MTT assay against MCF-7 cells. Rabbits were used as experimental animals for the assessment of various in vivo pharmacokinetic parameters of selected formulations. The pharmacokinetic parameters such as C_max (1.18–1.33 folds), AUC_0-t (39.38–46.55 folds), MRT (10.04–12.79 folds), t_1/2 (3.06–4.6 folds), and V_d (6.96–8.38 folds) have been increased significantly while clearance (4.34–4.61 folds) has been decreased significantly for the selected nanoformulations as compared to commercially available paclitaxel formulation (Paclixil^®). The surface conjugation of nanoparticles with trastuzumab resulted in an increase in in vitro cytotoxicity as compared to plain nanoformulations and commercially available conventional brand (Paclixil^®). The developed PLGA-paclitaxel nanoformulations conjugated with trastuzumab have the desired physiochemical characteristics, surface morphology, sustained release kinetics, and enhanced targeting.

Tailored Bioactive Compost from Agri-Waste Improves the Growth and Yield of Chili Pepper and Tomato

An extensive use of chemical fertilizers has posed a serious impact on food and environmental quality and sustainability. As the organic and biofertilizers can satisfactorily fulfill the crop’s nutritional requirement, the plants require less chemical fertilizer application; hence, the food is low in chemical residues and environment is less polluted. The agriculture crop residues, being a rich source of nutrients, can be used to feed the soil and crops after composting and is a practicable approach to sustainable waste management and organic agriculture instead of open-field burning of crop residues. This study demonstrates a feasible strategy to convert the wheat and rice plant residues into composted organic fertilizer and subsequent enrichment with plant-beneficial bacteria. The bioactive compost was then tested in a series of in vitro and in vivo experiments for validating its role in growing organic vegetables. The compost was enriched with a blend of micronutrients, such as zinc, magnesium, and iron, and a multi-trait bacterial consortium AAP (Azospirillum, Arthrobacter, and Pseudomonas spp.). The bacterial consortium AAP showed survival up to 180 days post-inoculation while maintaining their PGP traits. Field emission scanning electron microscopic analysis and fluorescence in situ hybridization (FISH) of bioactive compost further elaborated the morphology and confirmed the PGPR survival and distribution. Plant inoculation of this bioactive compost showed significant improvement in the growth and yield of chilies and tomato without any additional chemical fertilizer yielding a high value to cost ratio. An increase of ≈35% in chlorophyll contents, ≈25% in biomass, and ≈75% in yield was observed in chilies and tomatoes. The increase in N was 18.7 and 25%, while in P contents were 18.5 and 19% in chilies and tomatoes, respectively. The application of bioactive compost significantly stimulated the bacterial population as well as the phosphatase and dehydrogenase activities of soil. These results suggest that bioactive compost can serve as a source of bioorganic fertilizer to get maximum benefits regarding vegetable yield, soil quality, and fertilizer saving with the anticipated application for other food crops. It is a possible win-win situation for environmental sustainability and food security.

Serum Resistin Levels and Related Genetic Variants Are Associated With Bone Mineral Density in Postmenopausal Women

BACKGROUND

Osteoporosis is a multifactorial disorder and a number of genetic variants or loci responsible for bone mineral density (BMD) have been identified. Resistin, a novel adipokine has diverse role in human body including its function in bone remodeling. The objective of this study was to see the association of serum resistin levels and related genetic variants (rs3931020, rs13144478) with BMD in postmenopausal females.

METHODS

This comparative analytical study was conducted on postmenopausal osteoporotic (n=101), osteopenic (n=77) and non-osteoporotic (n=74) females. For comparison and correlational analysis, Kruskal-Wallis test and Spearman's rho correlation were used respectively. Hardy-Weinberg equilibrium (HWE) was calculated by using Chi-square test (χ²).

RESULTS

There was significant difference in the serum levels of resistin (p <0.001), among the three groups. Significant negative correlation of resistin was observed with BMD at various sites. Serum resistin levels were significantly low in the rs3931020 AA homozygous genotype (p = 0.010), and significantly high in the rs13144478 AT heterozygous genotype (p = 0.020), BMD at all sites except left femoral neck was significantly high in rs3931020 AA genotype, while BMD at lumbar spine, left hip and total BMD were significantly low in the rs13144478 TT homozygotes.

CONCLUSION

High serum resistin levels are associated with low BMD and single nucleotide variation in rs3931020 and rs13144478 may lead to high serum resistin levels and low bone mineral density. Resistin can serve as a new genetic marker, potential therapeutic target and predictor of osteoporosis.

Association of serum levels of Visfatin, Intelectin-1, RARRES2 and their genetic variants with bone mineral density in postmenopausal females

BACKGROUND

Adipokines are engaged in bone physiology and regulate bone mineral density (BMD) by playing protective or cynical role in bone metabolism. The study is designed to measure and compare BMD, adipokines (retinoic acid receptor responder protein-2 RARRES2, visfatin and Intelectin-1) and their genetic variants in postmenopausal osteoporotic, osteopenic and non-osteoporotic females.

METHODS

This comparative study included postmenopausal non-osteoporotic (n=72), osteopenic (n=72) and osteoporotic (n=100) females with two years of amenorrhea and age between 50 to 70 years. Gold standard DXA was used to measure BMD. Hardy-Weinberg equilibrium was established. Kruskal-Wallis test for comparisons, logistic and multivariate regression analysis were used to rule out the predictors of BMD.

RESULTS

On comparing the three groups, significant differences were observed in serum RARRES2 (p <0.001) and serum visfatin (p=0.050). The significant positive predictor of BMD at lumbar spine and total hip was serum visfatin. BMD at right and left femoral neck was predicted negatively by serum chemerin while BMD at left femoral neck was also predicted positively by serum calcium levels. There was significant difference in BMD at right femoral neck (p = 0.033) between rs7806429 genotypes. The odds of having low BMD increases with increasing serum levels of chemerin and decreasing serum levels of visfatin and calcium.

CONCLUSION

The adipokines RARRES2 and visfatin are associated with BMD. RARRES2 is an independent negative and visfatin is positive predictor of BMD in postmenopausal females. BMD at right femoral neck was significantly low in RARRES2 rs7806429 TC heterozygotes.

qPCR Analysis Reveals Association of Differential Expression of SRR, NFKB1, and PDE4B Genes With Type 2 Diabetes Mellitus

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a heterogeneous, metabolic, and chronic condition affecting vast numbers of the world's population. The related variables and T2DM associations have not been fully understood due to their diverse nature. However, functional genomics can facilitate understanding of the disease. This information will be useful in drug design, advanced diagnostic, and prognostic markers.

AIM

To understand the genetic causes of T2DM, this study was designed to identify the differentially expressed genes (DEGs) of the disease.

METHODS

We investigated 20 publicly available disease-specific cDNA datasets from Gene Expression Omnibus (GEO) containing several attributes including gene symbols and clone identifiers, GenBank accession numbers, and phenotypic feature coordinates. We analyzed an integrated system-level framework involving Gene Ontology (GO), protein motifs and co-expression analysis, pathway enrichment, and transcriptional factors to reveal the biological information of genes. A co-expression network was studied to highlight the genes that showed a coordinated expression pattern across a group of samples. The DEGs were validated by quantitative PCR (qPCR) to analyze the expression levels of case and control samples (50 each) using glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as the reference gene.

RESULTS

From the list of 50 DEGs, we ranked three T2DM-related genes (p < 0.05): SRR, NFKB1, and PDE4B. The enriched terms revealed a significant functional role in amino acid metabolism, signal transduction, transmembrane and intracellular transport, and other vital biological functions. DMBX1, TAL1, ZFP161, NFIC (66.7%), and NR1H4 (33.3%) are transcriptional factors associated with the regulatory mechanism. We found substantial enrichment of insulin signaling and other T2DM-related pathways, such as valine, leucine and isoleucine biosynthesis, serine and threonine metabolism, adipocytokine signaling pathway, P13K/Akt pathway, and Hedgehog signaling pathway. The expression profiles of these DEGs verified by qPCR showed a substantial level of twofold change (FC) expression (2-ΔΔCT) in the genes SRR (FC ≤ 0.12), NFKB1 (FC ≤ 1.09), and PDE4B (FC ≤ 0.9) compared to controls (FC ≥ 1.6). The downregulated expression of these genes is associated with pathophysiological development and metabolic disorders.

CONCLUSION

This study would help to modulate the therapeutic strategies for T2DM and could speed up drug discovery outcomes.