Effect of Phosphatidylethanolamine on Pore Formation Induced by the Antimicrobial Peptide PGLa.

Most antimicrobial peptides (AMPs) induce pore formation and a burst of lipid bilayers and plasma membranes. This causes severe leakage of the internal contents and cell death. The AMP PGLa forms nanopores in giant unilamellar vesicles (GUVs) comprising dioleoylphosphatidylcholine (DOPC) and dioleoylphosphatidylglycerol (DOPG). We here elucidated the effect of the line tension of a prepore rim on PGLa-induced nanopore formation by investigating the interaction of PGLa with single GUVs comprising dioleoylphosphatidylethanolamine (DOPE)/DOPG (6:4) in buffer using the single GUV method. We found that PGLa forms nanopores in the GUV membrane, which evolved into a local burst and burst of GUVs. The rate of pore formation in DOPE/DOPG-GUVs was smaller than that in DOPC/DOPG-GUVs. PGLa is located only in the outer leaflet of a GUV bilayer just before a fluorescent probe AF647 leakage from the inside, indicating that this asymmetric distribution induces nanopore formation. PGLa-induced local burst and burst of GUVs were observed at 10 ms-time resolution. After nanopore formation started, dense particles and small vesicles appeared in the GUVs, followed by a decrease in the GUV diameter. The GUV was finally converted into smaller GUV or lipid membrane aggregates. We discuss the mechanisms of PGLa-induced nanopore formation and its direct evolution to a GUV burst.

Construction and investigation of multi-enzyme immobilized matrix for the production of HFCS.

Enzymes are biological molecules that act as catalysts and speed up the biochemical reactions. The world's biotechnological ventures are development of enzyme productiveness, and advancement of novel techniques for thriving their shelf existence. Nowadays, the most burning questions in enzyme technology are how to improve the enzyme productivity and reuse them. The immobilization of enzymes provides an excellent scope to reuse the enzymes several times to increase productivity. The main aim of the present study is the establishment of an immobilized multi-enzyme bio-system engineering process for the production of High-fructose corn syrup (HFCS) with an industrial focus. In this study, multi-enzyme such as α-amylase, glucoamylase and glucose isomerase were immobilized in various support matrices like sodium alginate, sawdust, sugarcane bagasse, rice bran and combination of alginate with cellulosic materials. The activities of the immobilized multi-enzyme system for the production of HFCS from the starch solution were determined. The multi-enzyme immobilized in sodium alginate shows better fructose conversion than free enzyme. Among the support matrices, multi-enzyme immobilized in sawdust produced total 80.74 mg/mL of fructose from starch solution and it was able to be used in several production cycles. On the other hand, multi-enzyme immobilized in combination of sodium alginate and sawdust produced the maximum amount of fructose (total 84.82 mg/mL). The free enzyme produced 17.25 mg/mL of fructose from the starch solution in only a single cycle. In this study a new fixed bed immobilized multi-enzyme bioreactor system was developed for the production of HFCS directly from starch. This finding will create a new opportunity for the application of immobilized multi-enzyme systems in many sectors of industrial biotechnology.

The Impact of mHealth Education on Changing Menstrual Hygiene Management Knowledge and Practices Among School-Going Adolescent Girls in Rural Bangladesh: A Quasi-experimental Study Protocol.

BACKGROUND: Menstruation is a normal physiological process for women during their reproductive cycle, typically beginning during adolescence. During this stage, lack of knowledge, social taboos, and shyness act as barriers to proper menstrual hygiene management, rendering adolescent girls more vulnerable. This issue is highly prevalent in low- and middle-income countries. In rural areas of Bangladesh, there is a deficiency in menstrual hygiene management due to inadequate information and knowledge among adolescent girls. Therefore, this study aims to assess the effect of mHealth education on the knowledge and practices of menstrual hygiene management among school-going adolescent girls in rural Bangladesh. METHODS: This is a quasi-experimental study conducted from early June to December 2023 at a secondary high school in Chandpur, Bangladesh. Participants' data will be collected through face-to-face interviews using a structured questionnaire covering socioeconomics, knowledge of menstrual hygiene management, and practices. Pre-test data will be collected at baseline, followed by a 6-month mHealth education intervention. Afterward, post-test data will be collected using the same questionnaire. The data will be analyzed as frequency and percentage for descriptive statistics, and a paired t-test will be used to compare the pre-and post-test data. RESULTS: In the study, 172 participants were enrolled at baseline. Among them, 69.8% were aged 10-14 years. The outcome of this study will be published in a peer-reviewed journal. The findings will provide evidence-based information for the government, researchers, and policymakers on menstrual hygiene management using mobile health technology. CONCLUSION: mHealth education can be posited as a significant tool for increasing knowledge and practices related to menstrual hygiene management in rural regions of Bangladesh.

Deep analysis of adsorption isotherm for rapid sorption of Acid Blue 93 and Reactive Red 195 on reactive graphene.

Graphene-based adsorbent was prepared by adopting a green synthetic route via the chemical exfoliation of graphite and low-temperature thermal activation. Prepared reactive graphene (RG) was characterized through various techniques, and its adsorption capabilities for textile dye removal were investigated for Acid Blue-93 (AB) and Reactive Red-195 (RR) under different operational conditions. The dye sorption equilibrium and mechanism were comprehensively studied using isotherm and kinetic models and compared statistically to explain the sorption behavior. Results show AB and RR adsorption by RG attains equilibrium in 60 min and 70 min, with a high sorption quantity of 397 mg g-1 and 262 mg g-1 (initial dye concentration of 100 mg L-1), respectively. The dye sorption anticipates that the high surface area (104.52 m2 gm-1) and constructed meso-macroporous features of RG facilitated the interaction between the dye molecules and graphitic skeleton. The R-P isotherm fitted the best of equilibrium data, having the least variance in residuals for both dyes (AB = 0.00031 and RR = 0.00047). The pseudo-second order model best fitted the kinetics of sorption on RG, with chemisorption being the predominant process delimiting step. The overall results promise the dye removal capability of RG to be an efficient adsorbent for azo-based dyes from textile effluents.

Effect of membrane tension on antimicrobial peptide PGLa-induced pore formation in lipid bilayers.

The osmotic pressure (Π) method has recently been developed to quantitatively examine the effect of membrane tension (σ) on pore formation in giant unilamellar vesicles (GUVs) induced by antimicrobial peptides (AMPs). Here, we used the Π method to reveal the effect of σ on the interaction of an AMP, PGLa, with lipid bilayers comprising dioleoylphosphatidylglycerol (DOPG) and dioleoylphosphatidylcholine (DOPC) (4/6). PGLa induced leakage of fluorescent probes from single GUVs under Π, indicating nanopore formation. Membrane tension did not transform a PGLa-induced nanopore into a micropore nor cause GUV burst up to 3.4 mN/m, which is in contrast with the effect of σ on another AMP, magainin 2-induced pore formation, where lower σ resulted in GUV burst. The fraction of leaking GUVs at a specific time increased with increasing σ, indicating that the rate of PGLa-induced pore formation increases with increasing σ. The rate of transfer of fluorescent probe-labeled PGLa across the lipid bilayer without pore formation also increased with increasing σ. PGLa-induced pore formation requires a symmetric distribution of peptides in both leaflets of the GUV bilayer, and thus we infer that the increase in the rate of PGLa transfer from the outer leaflet to the inner leaflet underlies the increase in the rate of pore formation with increasing σ. On the basis of these results, we discuss the difference between the effect of σ on nanopore formation in GUV membranes induced by PGLa and that by magainin 2.

Bronchogenic cyst at unusual location.

Bronchogenic cyst results from abnormal bronchial budding. Thin-stalked mobile bronchogenic cysts are rare and sometimes radiologically mimic mass lesion, making preoperative diagnosis difficult. We present a 12-year-old boy with a preoperative diagnosis of intraparenchymal cystic lung lesion misled by radiology. We performed a mini-thoracotomy revealing a thin stalked mobile elongated cyst that arose from the right inferior pulmonary ligament, confirmed as a bronchogenic cyst in histopathology.

Systematic review of Internet of medical things for cardiovascular disease prevention among Australian first nations.

Chronic diseases within Indigenous communities constitute the most compelling ill-health burdens and treatment inequalities, particularly in rural and remote Australia. In response to these vital issues, a systematic literature review of the adoption of wearable, Artificial Intelligence-driven, electrocardiogram sensors, in a telehealth Internet of Medical Things (IoMT) context was conducted to scale up rural Indigenous health. To this end, four preselected scientific databases were chosen for data extraction to align with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique. From the initially collected (n=4436) articles, a total of 32 articles were analysed, being synthesised from the review inclusion criteria, maintaining strict eligibility and eliminating duplicates. None of the various studies found on this innovative healthcare intervention has given a comprehensive picture of how this could be an effective method of care dedicated to rural Indigenous communities with cardiovascular diseases (CVDs). Herein, we presented the unique concepts of IoMT-driven wearable biosensors tailored for rural indigenous cardiac patients, their clinical implications, and cardiovascular disease management within the telehealth domain. This work contributes to understanding the adoption of wearable IoMT sensor-driven telehealth model, highlighting the need for real-time data from First Nations patients in rural and remote areas for CVD prevention. Pertinent implications, research impacts, limitations and future research directions are endorsed, securing long-term Wearable IoMT sensor-driven telehealth sustainability.

Relationship between antimicrobial peptides-induced cell membrane damage and bactericidal activity.

Most antimicrobial peptides (AMPs) act by killing bacterial cells. However, there is little information regarding the required interaction time between AMPs and bacterial cells to exert the bactericidal activity. One of the causes of the bactericidal activity is considered to be cell membrane damage, although little direct evidence is available. Here, we investigated the relationship between AMP-induced cell membrane damage in Escherichia coli and AMP-induced cell death at the single-cell level. Magainin 2, lactoferricin B, and PGLa were selected as the AMPs. First, we examined the interaction time (t) of AMPs with cells required to induce cell death using the single-cell analysis. The fraction of microcolonies containing only a single cell, Psingle (t), which indicates the fraction of dead cells, increased with time to reach ∼1 in a short time (≤5 min). Then, we examined the interaction between AMPs and single cells using confocal laser scanning microscopy in the presence of membrane-impermeable SYTOX green. Within a short time interaction, the fluorescence intensity of the cells due to SYTOX green increased, indicating that AMPs induced cell membrane damage through which the dye entered the cytoplasm. The fraction of cells in which SYTOX green entered the cytoplasm among all examined cells after the interaction time (t), Pentry (t), increased with time, reaching ∼1 in a short time (≤5 min). The values of Psingle (t) and Pentry (t) were similar at t ≥ 3 min for all AMPs. The bindings of AMPs to cells were largely reversible, whereas the AMP-induced cell membrane damages were largely irreversible because SYTOX green entered the cells after dilution of AMP concentration. Based on these results, we conclude that the rapid, substantial membrane permeabilization of cytoplasmic contents after a short interaction time with AMPs and the residual damage after dilution induce cell death.

Molecular Biomarker Identification Using a Network-Based Bioinformatics Approach That Links COVID-19 With Smoking.

The COVID-19 coronavirus, which primarily affects the lungs, is the source of the disease known as SARS-CoV-2. According to "Smoking and COVID-19: a scoping review," about 32% of smokers had a severe case of COVID-19 pneumonia at their admission time and 15% of non-smokers had this case of COVID-19 pneumonia. We were able to determine which genes were expressed differently in each group by comparing the expression of gene transcriptomic datasets of COVID-19 patients, smokers, and healthy controls. In all, 37 dysregulated genes are common in COVID-19 patients and smokers, according to our analysis. We have applied all important methods namely protein-protein interaction, hub-protein interaction, drug-protein interaction, tf-gene interaction, and gene-MiRNA interaction of bioinformatics to analyze to understand deeply the connection between both smoking and COVID-19 severity. We have also analyzed Pathways and Gene Ontology where 5 significant signaling pathways were validated with previous literature. Also, we verified 7 hub-proteins, and finally, we validated a total of 7 drugs with the previous study.

Integrated bioinformatics and statistical approach to identify the cmmon mlecular mchanisms of oesity that are linked to the development of two psychiatric disorders: Schizophrenia and major depressive disorder.

Obesity is a chronic multifactorial disease characterized by the accumulation of body fat and serves as a gateway to a number of metabolic-related diseases. Epidemiologic data indicate that Obesity is acting as a risk factor for neuro-psychiatric disorders such as schizophrenia, major depression disorder and vice versa. However, how obesity may biologically interact with neurodevelopmental or neurological psychiatric conditions influenced by hereditary, environmental, and other factors is entirely unknown. To address this issue, we have developed a pipeline that integrates bioinformatics and statistical approaches such as transcriptomic analysis to identify differentially expressed genes (DEGs) and molecular mechanisms in patients with psychiatric disorders that are also common in obese patients. Biomarker genes expressed in schizophrenia, major depression, and obesity have been used to demonstrate such relationships depending on the previous research studies. The highly expressed genes identify commonly altered signalling pathways, gene ontology pathways, and gene-disease associations across disorders. The proposed method identified 163 significant genes and 134 significant pathways shared between obesity and schizophrenia. Similarly, there are 247 significant genes and 65 significant pathways that are shared by obesity and major depressive disorder. These genes and pathways increase the likelihood that psychiatric disorders and obesity are pathogenic. Thus, this study may help in the development of a restorative approach that will ameliorate the bidirectional relation between obesity and psychiatric disorder. Finally, we also validated our findings using genome-wide association study (GWAS) and whole-genome sequence (WGS) data from SCZ, MDD, and OBE. We confirmed the likely involvement of four significant genes both in transcriptomic and GWAS/WGS data. Moreover, we have performed co-expression cluster analysis of the transcriptomic data and compared it with the results of transcriptomic differential expression analysis and GWAS/WGS.

Bioinformatics approach to analyse COVID-19 biomarkers accountable for generation of intracranial aneurysm in COVID-19 patients.

COVID-19 became a health emergency on January 30, 2020. SARS-CoV-2 is the causative agent of the coronavirus disease known as COVID-19 and can develop cardiometabolic and neurological disorders. Intracranial aneurysm (IA) is considered the most significant reason for hemorrhagic stroke,and it accounts for approximately 85% of all subarachnoid hemorrhages (SAH). Retinoid signaling abnormalities may explain COVID-19's pathogenesis with inhibition of AEH2, from which COVID-19 infection may enhance aneurysm formation and rupture due to abrupt blood pressure changes, endothelial cell injury, and systemic inflammation. The objective of this study was to investigate the potential biomarkers, differentially expressed genes (DEGs), and metabolic pathways associated with both COVID-19 and intracranial aneurysm (IA) using simulation databases like DIsGeNET. The purpose was to confirm prior findings and gain a comprehensive understanding of the underlying mechanisms that contribute to the development of these conditions. We combined the regulated genes to describe intracranial aneurysm formation in COVID-19. To determine DEGs in COVID-19 and IA patient tissues, we compared gene expression transcriptomic datasets from healthy and diseased individuals. There were 41 differentially expressed genes (DEGs) shared by both the COVID-19 and IA datasets (27 up-regulated genes and 14 down-regulated genes). Using protein-protein interaction analysis, we were able to identify hub proteins (C3, NCR1, IL10RA, OXTR, RSAD2, CD38, IL10RB, MX1, IL10, GFAP, IFIT3, XAF1, USP18, OASL, IFI6, EPSTI1, CMPK2, and ISG15), which were not described as key proteins for both COVID-19 and IA before. We also used Gene Ontology analysis (6 significant ontologies were validated), Pathway analysis (the top 20 were validated), TF-Gene interaction analysis, Gene miRNA analysis, and Drug-Protein interaction analysis methods to comprehend the extensive connection between COVID-19 and IA. In Drug-Protein interaction analysis, we have gotten the following three drugs: LLL-3348, CRx139, and AV41 against IL10 which was both common for COVID-19 and IA disease. Our study with different cabalistic methods has showed the interaction between the proteins and pathways with drug analysis which may direct further treatment development for certain diseases.

Bioinformatics and network biology approach to identifying type 2 diabetes genes and pathways that influence the progression of breast cancer.

Breast cancer is the second most prevalent malignancy affecting women. Postmenopausal women breast tumor is one of the top causes of death in women, accounting for 23% of cancer cases. Type 2 diabetes, a worldwide pandemic, has been connected to a heightened risk of several malignancies, although its association with breast cancer is still uncertain. In comparison to non-diabetic women, women with T2DM had a 23% elevated likelihood of developing breast cancer. It is difficult to determine causative or genetic susceptibility that connect T2DM and breast cancer. We created a large-scale network-based quantitative approach employing unbiased methods to discover abnormally amplified genes in both T2DM and breast cancer, to solve these issues. We performed transcriptome analysis to uncover identical genetic biomarkers and pathways to clarify the connection between T2DM and breast cancer patients. In this study, two RNA-seq datasets (GSE103001 and GSE86468) from the Gene Expression Omnibus (GEO) are used to identify mutually differentially expressed genes (DEGs) for breast cancer and T2DM, as well as common pathways and prospective medicines. Firstly, 45 shared genes (30 upregulated and 15 downregulated) between T2D and breast cancer were detected. We employed gene ontology and pathway enrichment to characterize prevalent DEGs' molecular processes and signal transduction pathways and observed that T2DM has certain connections to the progression of breast cancer. Using several computational and statistical approaches, we created a protein-protein interactions (PPI) network and revealed hub genes. These hub genes can be potential biomarkers, which may also lead to new therapeutic strategies for investigated diseases. We conducted TF-gene interactions, gene-microRNA interactions, protein-drug interactions, and gene-disease associations to find potential connections between T2DM and breast cancer pathologies. We assume that the potential drugs that emerged from this study could be useful therapeutic values. Researchers, doctors, biotechnologists, and many others may benefit from this research.

Bioinformatics and In silico approaches to identify novel biomarkers and key pathways for cancers that are linked to the progression of female infertility: A comprehensive approach for drug discovery.

Despite modern treatment, infertility remains one of the most common gynecologic diseases causing severe health effects worldwide. The clinical and epidemiological data have shown that several cancerous risk factors are strongly linked to Female Infertility (FI) development, but the exact causes remain unknown. Understanding how these risk factors affect FI-affected cell pathways might pave the door for the discovery of critical signaling pathways and hub proteins that may be targeted for therapeutic intervention. To deal with this, we have used a bioinformatics pipeline to build a transcriptome study of FI with four carcinogenic risk factors: Endometrial Cancer (EC), Ovarian Cancer (OC), Cervical Cancer (CC), and Thyroid Cancer (TC). We identified FI sharing 97, 211, 87 and 33 differentially expressed genes (DEGs) with EC, OC, CC, and TC, respectively. We have built gene-disease association networks from the identified genes based on the multilayer network and neighbour-based benchmarking. Identified TNF signalling pathways, ovarian infertility genes, cholesterol metabolic process, and cellular response to cytokine stimulus were significant molecular and GO pathways, both of which improved our understanding the fundamental molecular mechanisms of cancers associated with FI progression. For therapeutic intervention, we have targeted the two most significant hub proteins VEGFA and PIK3R1, out of ten proteins based on Maximal Clique Centrality (MCC) value of cytoscape and literature analysis for molecular docking with 27 phytoestrogenic compounds. Among them, sesamin, galangin and coumestrol showed the highest binding affinity for VEGFA and PIK3R1 proteins together with favourable ADMET properties. We recommended that our identified pathway, hub proteins and phytocompounds may be served as new targets and therapeutic interventions for accurate diagnosis and treatment of multiple diseases.

Features and risk factors of post-COVID-19 syndrome: findings from a longitudinal study in Bangladesh.

Background: A comprehensive study of the post-COVID syndrome (PCS) remains scarce in low-and middle-income countries. We assessed the prevalence, incidence rate, evolution over time, and risk factors of PCS among hospitalized (HS) and non-hospitalized (NHS) COVID-19 survivors. Methods: We undertook a prospective longitudinal study of COVID-19 survivors at months 1, 3, and 5 post-discharge or post-isolation period. The study was conducted at two COVID-19-designated hospitals in Dhaka, Bangladesh, between December 2020 and October 2021. Findings: 362 participants were enrolled in the study; the median time from the onset of COVID-19 to enrolment was 57 days (IQR 41, 82). At enrolment, after adjusting for potential confounders, the HS more often had one or more symptoms, peripheral neuropathy (PN), depression and anxiety disorder, poor quality of life, dyspnea, tachycardia, restrictive lung disease on spirometry, anemia, proteinuria, and need for insulin therapy than the non-hospitalized group (95% CI > 1 for all). Although most of these findings decreased significantly over time in HS, PN increased in both groups. The incidence of diabetes was 9.8/1000 person-month, and the new requirement of insulin therapy was higher (aOR, 6.71; 95% CI, 2.87, 15.67) among HS than the NHS. Older age, being female, comorbidity, cigarette smoking, hospitalization, and contact with COVID-19 cases were independently associated with PCS. Interpretation: We observed a high burden of PCS in hospitalized and non-hospitalized survivors despite most findings' decreasing trend over time. Our results underscore the importance of continuing long-term follow-up and subsequent management. Funding: The United States Agency for International Development (USAID).

Bioinformatics Strategies to Identify Shared Molecular Biomarkers That Link Ischemic Stroke and Moyamoya Disease with Glioblastoma.

Expanding data suggest that glioblastoma is accountable for the growing prevalence of various forms of stroke formation, such as ischemic stroke and moyamoya disease. However, the underlying deterministic details are still unspecified. Bioinformatics approaches are designed to investigate the relationships between two pathogens as well as fill this study void. Glioblastoma is a form of cancer that typically occurs in the brain or spinal cord and is highly destructive. A stroke occurs when a brain region starts to lose blood circulation and prevents functioning. Moyamoya disorder is a recurrent and recurring arterial disorder of the brain. To begin, adequate gene expression datasets on glioblastoma, ischemic stroke, and moyamoya disease were gathered from various repositories. Then, the association between glioblastoma, ischemic stroke, and moyamoya was established using the existing pipelines. The framework was developed as a generalized workflow to allow for the aggregation of transcriptomic gene expression across specific tissue; Gene Ontology (GO) and biological pathway, as well as the validation of such data, are carried out using enrichment studies such as protein-protein interaction and gold benchmark databases. The results contribute to a more profound knowledge of the disease mechanisms and unveil the projected correlations among the diseases.

Statistical Bioinformatics to Uncover the Underlying Biological Mechanisms That Linked Smoking with Type 2 Diabetes Patients Using Transcritpomic and GWAS Analysis.

Type 2 diabetes (T2D) is a chronic metabolic disease defined by insulin insensitivity corresponding to impaired insulin sensitivity, decreased insulin production, and eventually failure of beta cells in the pancreas. There is a 30-40 percent higher risk of developing T2D in active smokers. Moreover, T2D patients with active smoking may gradually develop many complications. However, there is still no significant research conducted to solve the issue. Hence, we have proposed a highthroughput network-based quantitative pipeline employing statistical methods. Transcriptomic and GWAS data were analysed and obtained from type 2 diabetes patients and active smokers. Differentially Expressed Genes (DEGs) resulted by comparing T2D patients' and smokers' tissue samples to those of healthy controls of gene expression transcriptomic datasets. We have found 55 dysregulated genes shared in people with type 2 diabetes and those who smoked, 27 of which were upregulated and 28 of which were downregulated. These identified DEGs were functionally annotated to reveal the involvement of cell-associated molecular pathways and GO terms. Moreover, protein-protein interaction analysis was conducted to discover hub proteins in the pathways. We have also identified transcriptional and post-transcriptional regulators associated with T2D and smoking. Moreover, we have analysed GWAS data and found 57 common biomarker genes between T2D and smokers. Then, Transcriptomic and GWAS analyses are compared for more robust outcomes and identified 1 significant common gene, 19 shared significant pathways and 12 shared significant GOs. Finally, we have discovered protein-drug interactions for our identified biomarkers.

Prevalence, Predictive Factors, and Outcomes of Respiratory Failure in Children With Pneumonia Admitted in a Developing Country.

Background: Pneumonia has been the leading infectious cause of morbidity and mortality in children under 5 years of age for the last several decades. Although most of these deaths occur due to respiratory failure, published data are limited regarding predicting factors and outcomes of respiratory failure in children hospitalized with pneumonia or severe pneumonia. Objective: This study aimed to explore the prevalence, predicting factors, and outcomes of respiratory failure in children under-five with pneumonia or severe pneumonia. Methods: In this retrospective chart analysis, we enrolled children under 5 years of age hospitalized with pneumonia or severe pneumonia in the Dhaka Hospital of International Centre for Diarrheal Disease Research, Bangladesh (icddr,b) between August 2013 and December 2017. Comparisons were made between children with respiratory failure (n = 212) and those without respiratory failure (n = 4,412). Respiratory failure was defined when the oxygen saturation/fraction of inspired oxygen (SpO2/FiO2) was <315. Results: A total of 4,625 children with pneumonia or severe pneumonia were admitted during this study period. Among them, 212 (4.6%) children developed respiratory failure and formed the case group. A total of 4,412 (95.3%) children did not develop respiratory failure and formed the comparison group. In logistic regression analysis, after adjusting with potential confounders, severe sepsis [adjusted odds ratio (aOR): 12.68, 95% CI: 8.74-18.40], convulsion (aOR: 4.52, 95% CI: 3.06-6.68), anemia (aOR: 1.76, 95% CI: 1.20-2.57), and severe underweight (aOR: 1.97, 95% CI: 1.34-2.89) were found to be independently associated with respiratory failure. As expected, children with respiratory failure more often had fatal outcome than without respiratory failure (74, 1%, p < 0.001). Conclusion: The results of our analyses revealed that prevalence of respiratory failure was 4.6% among under-five children hospitalized for pneumonia or severe pneumonia. Severe sepsis, convulsion, anemia, and severe underweight were the independent predictors for respiratory failure in such children and their case-fatality rate was significantly higher than those without respiratory failure. Early recognition of these predicting factors of respiratory failure may help clinicians imitating prompt treatment that may further help to reduce deaths in such children, especially in resource-limited settings.

Bioinformatics Approach to Identify Significant Biomarkers, Drug Targets Shared Between Parkinson's Disease and Bipolar Disorder: A Pilot Study.

Parkinson's disease (PD) is a neurodegenerative disorder responsible for shaking, rigidity, and trouble in walking and patients' coordination ability and physical stability deteriorate day by day. Bipolar disorder (BD) is a psychiatric disorder which is the reason behind extreme shiftiness in mood, and frequent mood inversion may reach too high called mania. People with BD have a greater chance of developing PD during the follow-up period. A lot of work has been done to understand the key factors for developing these 2 diseases. But the molecular functionalities that trigger the development of PD in people with BD are not clear yet. In our study, we are intended to identify the molecular biomarkers and pathways shared between BD and PD. We have investigated the RNA-Seq gene expression data sets of PD and BD. A total of 45 common unique genes (32 up-regulated and 13 down-regulated) abnormally expressed in both PD and BD were identified by applying statistical methods on the GEO data sets. Gene ontology (GO) and BioCarta, KEGG, and Reactome pathways analysis of these 45 common dysregulated genes identified numerous altered molecular pathways such as mineral absorption, Epstein-Barr virus infection, HTLV-I infection, antigen processing, and presentation. Analysis of protein-protein interactions revealed 9 significant hub-proteins, namely RPL21, RPL34, CKS2, B2M, TNFRSF10A, DTX2, HLA-B, ATP2A3, and TAPBP. Significant transcription factors (IRF8, SPI1, RUNX1, and FOXA1) and posttranscriptional regulator microRNAs (hsa-miR-491-3p and hsa-miR-1246) are also found by analyzing gene-transcription factors and gene-miRNAs interactions, respectively. Protein-drug interaction analysis revealed hub-protein B2M's interaction with molecular drug candidates like N-formylmethionine, 3-indolebutyric acid, and doxycycline. Finally, a link between pathological processes of PD and BD is identified at transcriptional level. This study may help us to predict the development of PD among the people suffering from BD and gives some clue to understand significant pathological mechanisms.

Hemp as a potential raw material toward a sustainable world: A review.

Global warming as a result of climate change has become a major concern for people all over the world. It has recently drawn the attention of the entire conscious community, with the fear that if not addressed properly, it will result in the extinction of numerous species around the world. At the same time, it will pose a threat to human health, food security, living environment and standard of living. Thereby, possible solutions are being explored accordingly; regulations have been imposed in places binding green production practices, limiting the emission of CO2 and emphasis is given on renewable resources along with the search for alternatives to carbon-positive materials. Cannabis sativa L. (hemp) has received a lot of attention because of its multipurpose usability, short production cycle, low capital demand in cultivation, possibility of carbon-negative transformation and easy carbon sequestering material. This paper reviews hemp as a very promising renewable resource including its potential uses in paper, textiles, composites, biofuel, and food industry.

Network based systems biology approach to identify diseasome and comorbidity associations of Systemic Sclerosis with cancers.

Systemic Sclerosis (SSc) is an autoimmune disease associated with changes in the skin's structure in which the immune system attacks the body. A recent meta-analysis has reported a high incidence of cancer prognosis including lung cancer (LC), leukemia (LK), and lymphoma (LP) in patients with SSc as comorbidity but its underlying mechanistic details are yet to be revealed. To address this research gap, bioinformatics methodologies were developed to explore the comorbidity interactions between a pair of diseases. Firstly, appropriate gene expression datasets from different repositories on SSc and its comorbidities were collected. Then the interconnection between SSc and its cancer comorbidities was identified by applying the developed pipelines. The pipeline was designed as a generic workflow to demonstrate a premise comorbid condition that integrate regarding gene expression data, tissue/organ meta-data, Gene Ontology (GO), Molecular pathways, and other online resources, and analyze them with Gene Set Enrichment Analysis (GSEA), Pathway enrichment and Semantic Similarity (SS). The pipeline was implemented in R and can be accessed through our Github repository: https://github.com/hiddenntreasure/comorbidity. Our result suggests that SSc and its cancer comorbidities share differentially expressed genes, functional terms (gene ontology), and pathways. The findings have led to a better understanding of disease pathways and our developed methodologies may be applied to any set of diseases for finding any association between them. This research may be used by physicians, researchers, biologists, and others.