Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants.

The escalation of global urbanization and industrial expansion has resulted in an increase in the emission of harmful substances into the atmosphere. Evaluating the effectiveness of titanium dioxide (TiO2) in photocatalytic degradation through traditional methods is resource-intensive and complex due to the detailed photocatalyst structures and the wide range of contaminants. Therefore in this study, recent advancements in machine learning (ML) are used to offer data-driven approach using thirteen machine learning techniques namely XG Boost (XGB), decision tree (DT), lasso Regression (LR2), support vector regression (SVR), adaBoost (AB), voting Regressor (VR), CatBoost (CB), K-Nearest Neighbors (KNN), gradient boost (GB), random Forest (RF), artificial neural network (ANN), ridge regression (RR), linear regression (LR1) to address the problem of estimation of TiO2 photocatalytic degradation rate of air contaminants. The models are developed using literature data and different methodical tools are used to evaluate the developed ML models. XGB, DT and LR2 models have high R2 values of 0.93, 0.926 and 0.926 in training and 0.936, 0.924 and 0.924 in test phase. While ANN, RR and LR models have lowest R2 values of 0.70, 0.56 and 0.40 in training and 0.62, 0.63 and 0.31 in test phase respectively. XGB, DT and LR2 have low MAE and RMSE values of 0.450 min-1/cm2, 0.494 min-1/cm2 and 0.49 min-1/cm2 for RMSE and 0.263 min-1/cm2, 0.285 min-1/cm2 and 0.29 min-1/cm2 for MAE in test stage. XGB, DT, and LR2 have 93% percent errors within 20% error range in training phase. XGB has 92% and DT, and LR2 have 94% errors with 20% range in test phase. XGB, DT, LR2 models remained the highest performing models and XGB is the most robust and effective in predictions. Feature importances reveal the role of input parameters in prediction made by developed ML models. Dosage, humidity, UV light intensity remain important experimental factors. This study will impact positively in providing efficient models to estimate photocatalytic degradation rate of air contaminants using TiO2.

Preformed Stainless-Steel Crowns: Their Effects on Oral Hygiene Maintenance and Gingival Health - A Prospective Original Research.

The stainless-steel crown (SSC) is a durable restoration and has several indications for use in primary teeth such as following a pulpotomy/pulpectomy, fractured teeth, teeth with developmental defects, or large multi-surface caries lesions where amalgam is likely to fail. Due to its durability and a lifespan like the primary tooth, it could well be the gold standard in restorative care. SSCs protect the crown from fracture, reduce the possibility for leakage, and ensure a biological seal. However, the placement of the SSC should follow a meticulous technique. There are some clinical situations where the SSC may fail, leading to plaque accumulation and gingivitis. This could be secondary to improper crimping of crown margins, which lead to poorly adapted SSC. In some clinical situations, ledge formation under the crown or failure to clean excess cement can contact the gingiva and cause gingival inflammation. This study was carried out on 41 children between the ages of 4 and 10 in Al Qassim region to study the effects of SSCs on gingiva and oral hygiene. The study also aims to establish the correlation between SSC adaptation and post-insertion inflammation. The plaque and gingival index were recorded at 3 months' post SSC insertion. Clinical examination was undertaken, and gingival index (Loe and Silness 1967) and plaque index (Silness and Loe 1967) were used to record gingival health and plaque accumulation, respectively. The result for post-inflammation and SSC adaptation showed that there was no statistically significant difference in post-insertion inflammation and crown adaptation (P value = 0.216). The result for pre-operative inflammation and post-operative inflammation shows that there is no significant difference in post-inflammation and adaptation (P value = 0.47). We found that oral hygiene care had a heightening effect and oral hygiene maintenance plays a key role in preventing gingival inflammation irrespective of the SSC adaptation over short periods of time (3 months).

Removal of Carbon Dioxide and Hydrogen Sulfide from Natural Gas Using a Hybrid Solvent of Monoethanolamine and N-Methyl 2-Pyrrolidone.

The main goal of traditional methods for sweetening natural gas (NG) is to remove hydrogen sulfide (H2S) and significantly lower carbon dioxide (CO2). However, when NG processes are integrated into the carbon capture and storage (CCS) framework, there is potential for synergy between these two technologies. A steady-state model utilizing a hybrid solvent consisting of N-methyl-2-pyrrolidone (NMP) and monoethanolamine (MEA) has been developed to successfully anticipate the CO2 and H2S capture process from NG. The model was tested against important variables affecting process performance. This article specifically explores the impact of operational parameters such as lean amine temperature, absorber pressure, and amine flow rate on the concentrations of CO2 and H2S in the sweet gas and reboiler duty. The result shows that hybrid solvents (MEA + NMP) perform better in removing acid gases and reducing reboiler duty than conventional chemical solvent MEA. The primary purpose is to meet product requirements while consuming the least energy possible, which is in line with any process plant's efficiency goals.

Navigating the future of solid oxide fuel cell: Comprehensive insights into fuel electrode related degradation mechanisms and mitigation strategies.

Solid Oxide Fuel Cells (SOFCs) have proven to be highly efficient and one of the cleanest electrochemical energy conversion devices. However, the commercialization of this technology is hampered by issues related to electrode performance degradation. This article provides a comprehensive review of the various degradation mechanisms that affect the performance and long-term stability of the SOFC anode caused by the interplay of physical, chemical, and electrochemical processes. In SOFCs, the most used anode material is nickel-yttria stabilized zirconia (Ni-YSZ) due to its advantages of high electronic conductivity and high catalytic activity for H2 fuel. However, various factors affecting the long-term stability of the Ni-YSZ anode, such as redox cycling, carbon coking, sulfur poisoning, and the reduction of the triple phase boundary length due to Ni particle coarsening, are thoroughly investigated. In response, the article summarizes the state-of-the-art diagnostic tools and mitigation strategies aimed at improving the long-term stability of the Ni-YSZ anode.

Evolutionary patterns and heterogeneity of Dengue Virus serotypes in Pakistan.

Comprehensive and systematic examination of Dengue virus (DENV) evolution is essential in the context of Pakistan as the virus presents a significant public health challenge with the ability to adapt and evolve. To shed light on intricate evolutionary patterns of all four DENV serotypes, we analyzed complete genome sequences (n=43) and envelope (E) gene sequences (n=44) of all four DENV serotypes collected in Pakistan from 1994 to 2023 providing a holistic view of their genetic evolution. Our findings revealed that all four serotypes of DENV co-circulate in Pakistan with a close evolutionary relationship between DENV-1 and DENV-3. Genetically distinct serotypes DENV-2 and DENV-4 indicate that DENV-4 stands out as the most genetically different, while DENV-2 exhibits greater complexity due to the presence of multiple genotypes and the possibility of temporal fluctuations in genotype prevalence. Selective pressure analysis in Envelope (E) gene revealed heterogeneity among sequences (n=44) highlighting 46 codons in the genome experiencing selective pressure, characterized by a bias towards balancing selection indicating genetic stability of the virus. Furthermore, our study suggested an intriguing evolutionary shift of DENV-4 towards the DENV-2 clade, potentially influenced by antibodies with cross-reactivity to multiple serotypes providing a critical insight into the complex factors shaping DENV evolution and contributing to the emergence of new serotype.

Biosafety measures for Alicyclobacillus spp. strains across various levels of biohazard.

Alicyclobacillus bacteria are important contaminants in the beverage industry because their spores remain in the product after usual pasteurization. At the same time, their impact on human health has yet to be characterized, as it is generally assumed to be low or non-existent. However, these bacteria are causing quality concerns mainly due to odor and taste changes of the product. Since potential health effects are not precisely known, an experimental assessment was performed, including a biosafety assessment of six viable and non-viable vegetative and spore forms of Alicyclobacillus spp. strains using cell cultures and rodent study. The monolayer of Caco-2 (Cancer coli-2) cells was investigated for its adsorption effect on the epithelium of the small intestine of mice. Lactate dehydrogenase leakage (LDH) and transepithelial electrical resistance (TEER) tests were used to ensure the integrity of the cell membrane and tight junctions. The methylthiazole tetrazolium bromide (MTT) assay examined in vitro cytotoxicity in Caco-2 and HepG2 cell lines. The hemolysis of erythrocytes was spectrophotometrically measured. The results showed negligible cytotoxicity or non-toxic response in mice. In conclusion, Alicyclobacillus spp. exhibited biocompatibility with negligible cytotoxicity and minimal safety concerns.

A randomized controlled trial of sweet basil leaf powder-enriched cookies for anemia management in adolescent girls.

The study aimed to evaluate the effectiveness of sweet basil leaf powder as a natural source of iron for the treatment of anemia in adolescent girls. Purposive sampling technique of two-stage sampling; part of the nonprobability sampling approach. Out of 2400 approached adolescent girls, 1645 agreed to participate and their nutritional status was assessed. Of these, 89.95% had clinical signs and symptoms of anemia, and 59.79% were found to be anemic based on Hb levels. From the anemic group, 65.18% were randomly selected to receive either B0 (Control), B1 (12.699 g FeSO4.7H2O/100 g), and B3 (16 g SBLP/100 g) cookies for 4 months. At the end of the intervention, the assessment of nutritional status, complete blood count, serum iron, serum ferritin, serum total iron-binding capacity (TIBC), and transferrin saturation was explored. Hematological parameters such as Hb, Hct, TIBC, MCV, MCH, MCHC, serum iron, and serum ferritin were significant (p ≤ .05). The result showed that the serum Fe was highest in group B3 while a significant decline was noted for group B0. Serum ferritin for B1 was better than B3. The entire treatment for transferrin saturation showed a highly significant increasing trend in B3 and B1, regardless of the control. TIBC levels raised in the control group while in all other treatments, it declined. The study demonstrated that SBLP-fortified cookies can be an effective treatment option for anemia, as evidenced by significant improvements in key hematological parameters.

Deep learning guided prediction modeling of dengue virus evolving serotype.

Evolution remains an incessant process in viruses, allowing them to elude the host immune response and induce severe diseases, impacting the diagnostic and vaccine effectiveness. Emerging and re-emerging diseases are among the significant public health concerns globally. The revival of dengue is mainly due to the potential for naturally arising mutations to induce genotypic alterations in serotypes. These transformations could lead to future outbreaks, underscoring the significance of studying DENV evolution in endemic regions. Predicting the emerging Dengue Virus (DENV) genome is crucial as the virus disrupts host cells, leading to fatal outcomes. Deep learning has been applied to predict dengue fever cases; there has been relatively less emphasis on its significance in forecasting emerging DENV serotypes. While Recurrent Neural Networks (RNN) were initially designed for modeling temporal sequences, our proposed DL-DVE generative and classification model, trained on complete genome data of DENV, transcends traditional approaches by learning semantic relationships between nucleotides in a continuous vector space instead of representing the contextual meaning of nucleotide characters. Leveraging 2000 publicly available DENV complete genome sequences, our Long Short-Term Memory (LSTM) based generative and Feedforward Neural Network (FNN) based classification DL-DVE model showcases proficiency in learning intricate patterns and generating sequences for emerging serotype of DENV. The generated sequences were analyzed along with available DENV serotype sequences to find conserved motifs in the genome through MEME Suite (version 5.5.5). The generative model showed an accuracy of 93 %, and the classification model provided insight into the specific serotype label, corroborated by BLAST search verification. Evaluation metrics such as ROC-AUC value 0.818, accuracy, precision, recall and F1 score, all to be around 99.00 %, demonstrating the classification model's reliability. Our model classified the generated sequences as DENV-4, exhibiting 65.99 % similarity to DENV-4 and around 63-65 % similarity with other serotypes, indicating notable distinction from other serotypes. Moreover, the intra-serotype divergence of sequences with a minimum of 90 % similarity underscored their uniqueness.

Corrigendum: Sesquiterpene lactones attenuate paclitaxel resistance via inhibiting MALAT1/STAT3/ FUT4 axis and P-Glycoprotein transporters in lung cancer cells.

[This corrects the article DOI: 10.3389/fphar.2022.795613.].

Effect of different preservatives on the physicochemical characteristics and shelf stability of Rasmalai: A comparative study.

Rasmalai is a very popular, delicious, and nutritious indigenous sweet dish in Indo-Pakistani civilization. It has a very short shelf life, i.e., up to 3 days. The study was designed to assess the effect of preservatives (potassium sorbate and calcium propionate) on the shelf stability of Rasmalai. Moreover, proximate composition and sensory evaluation of prepared Rasmalai were also carried out in the present study. In general, potassium sorbate and calcium propionate significantly increased the shelf life of Rasmalai. But treatment (R5) containing a combination of both potassium sorbate and calcium propionate (500 ppm each) improved its shelf life by up to 12 days by keeping good sensorial characteristics. The maximum total plate counts as well as yeast and molds were observed in control Rasmalai (without any preservatives) whereas minimum counts were found in R5 treatment containing a combination of both potassium sorbate and calcium propionate (500 ppm each). In conclusion, all the preservatives used in the present study were effective in enhancing the shelf life of Rasmalai but R5 treatment containing a combination of both potassium sorbate and calcium propionate (500 ppm each) was the most effective in enhancing shelf life without deleterious effect on sensorial characteristics.

Drug carrier wonders: Synthetic strategies of zeolitic imidazolates frameworks (ZIFs) and their applications in drug delivery and anti-cancer activity.

With the rapid advancement of nanotechnology, stimuli-responsive nanomaterials have emerged as a feasible choice for the designing of controlled drug delivery systems. Zeolitic imidazolates frameworks are a subclass of Metal-organic frameworks (MOFs) that are recognized by their excellent porosity, structural tunability and chemical modifications make them promising materials for loading targeted molecules and therapeutics agents. The biomedical industry uses these porous materials extensively as nano-carriers in drug delivery systems. These MOFs not only possess excellent targeted imaging ability but also cause the death of tumor cells drawing considerable attention in the current framework of anticancer drug delivery systems. In this review, the outline of stability, porosity, mechanism of encapsulation and release of anticancer drug have been reported extensively. In the end, we also discuss a brief outline of current challenges and future perspectives of ZIFs in the biomedical world.

Timosaponin Aâ ¢ induces drug-metabolizing enzymes by activating constitutive androstane receptor (CAR) via dephosphorylation of EGFR signaling pathway.

This study aims to assess the impact of Timosaponin AⅢ (T-AⅢ) on drug-metabolizing enzymes in anticancer treatment. In vivo experiments were conducted in nude mice and ICR mice. Following 24 days of T-AⅢ administration, nude mice exhibited induction of CYP2B10, MDR1, and CYP3A11 in the liver. In the liver of ICR mice, CYP2B10 and MDR1 were up-regulated after 3 days of T-AⅢ administration. In vitro assessments were conducted using HepG2 cells to ascertain the effects and underlying mechanisms. In HepG2 cells, T-AⅢ induced the expression of CYP2B6, MDR1, and CYP3A4, along with CAR activation. CAR siRNA reversed the T-AⅢ-induced increases in CYP2B6 and CYP3A4. Furthermore, other CAR target genes displayed significant up-regulation. Up-regulation of mCAR was observed in the livers of nude mice and ICR mice. Subsequent findings demonstrated that T-AⅢ activated CAR by inhibiting ERK1/2 phosphorylation, partially reversed by the MAPK/MEK activator t-BHQ. Inhibition of the ERK1/2 signaling pathway was also observed in vivo. Lastly, T-AⅢ inhibited the phosphorylation of EGFR at Tyr1173 and Tyr845, and it suppressed EGF-induced phosphorylation of EGFR, ERK, and CAR. Additionally, T-AⅢ inhibited EGFR phosphorylation in nude mice. Our results demonstrated that T-AⅢ is a novel CAR activator through inhibition of the EGFR pathway.

2D TiO2 nanosheets decorated via sphere-like BiVO4: a promising non-toxic material for liquid phase photocatalysis and bacterial eradication.

An in-depth investigation was conducted on a promising composite material (BiVO4/TiO2), focusing on its potential toxicity, photoinduced catalytic properties, as well as its antibiofilm and antimicrobial functionalities. The preparation process involved the synthesis of 2D-TiO2 using the lyophilization method, which was subsequently functionalized with sphere-like BiVO4. Finally, we developed BiVO4/TiO2 S-scheme heterojunctions which can greatly promote the separation of electron-hole pairs to achieve high photocatalytic performance. The evaluation of concentration- and time-dependent viability inhibition was performed on human lung carcinoma epithelial A549 cells. This assessment included the estimation of glutathione levels and mitochondrial dehydrogenase activity. Significantly, the BiVO4/TiO2 composite demonstrated minimal toxicity towards A549 cells. Impressively, the BiVO4/TiO2 composite exhibited notable photocatalytic performance in the degradation of rhodamine B (k =0.135 min-1) and phenol (k = 0.016 min-1). In terms of photoinduced antimicrobial performance, the composite effectively inactivated both gram-negative E. coli and gram-positive E. faecalis bacteria upon 60-min of UV-A light exposure, resulting in a significant log6(log10CFU/mL) reduction in bacterial count. These promising results can be attributed to the unique 2D morphology of TiO2 modified by sphere-like BiVO4, leading to an increased generation of (intracellular)hydroxyl radicals, which plays a crucial role in treatments of both organic pollutants and bacteria.

Diagnostic investigation of avian reovirus field variants circulating in broiler chickens in Pennsylvania of United States between 2017 and 2022.

Avian reovirus (ARV) has been continuously affecting the poultry industry in Pennsylvania (PA) in recent years. This report provides our diagnostic investigation on monitoring ARV field variants from broiler chickens in Pennsylvania. Genomic characterization findings of 72 ARV field isolates obtained from broiler cases during the last 6 years indicated that six distinct cluster variant strains (genotype I-VI), which were genetically diverse and distant from the vaccine and vaccine-related field strains, continuously circulated in PA poultry. Most of the variants clustered within genotype V (24/72, 33.3%), followed by genotype II (16/72, 22.2%), genotype IV (13/72, 18.1%), genotype III (13/72, 18.1%), genotype VI (05/72, 6.94%), and genotype I (1/72, 1.38%). The amino acid identity between 72 field variants and the vaccine strains (1133, 1733, 2408, 2177) varied from 45.3% to 99.7%, while the difference in amino acid counts ranged from 1-164. Among the field variants, the amino acid identity and count difference ranged from 43.3% to 100% and 0 to 170, respectively. Variants within genotype V had maximum amino acid identity (94.7-100%), whereas none of the variants within genotypes II and VI were alike. These findings indicate the continuing occurrence of multiple ARV genotypes in the environment.

Substitution of fishmeal: Highlights of potential plant protein sources for aquaculture sustainability.

High protein content, excellent amino acid profile, absence of anti-nutritional factors (ANFs), high digestibility and good palatability of fishmeal (FM), make it a major source of protein in aquaculture. Naturally derived FM is at risk due to an increase in its demand, unsustainable practices, and price. Thus, there is an urgent need to find affordable and suitable protein sources to replace FM. Plant protein sources are suitable due to their widespread availability and low cost. However, they contained certain ANFs, deficiency of some amino acids, low nutrient bioavailability and poor digestibility due to presence of starch and fiber. These unfavourable characteristics make them less suitable for feed as compared to FM. Thus, these potential challenges and limitations associated with various plant proteins have to be overcome by using different methods, i.e. enzymatic pretreatments, solvent extraction, heat treatments and fermentation, that are discussed briefly in this review. This review assessed the impacts of plant products on growth performance, body composition, flesh quality, changes in metabolic activities and immune response of fishes. To minimize the negative effects and to enhance nutritional value of plant products, beneficial functional additives such as citric acid, phytase and probiotics could be incorporated into the plant-based FM. Interestingly, these additives improve growth of fishes by increasing digestibility and nutrient utilization of plant based feeds. Overall, this review demonstrated that the substitution of fishmeal by plant protein sources is a plausible, technically-viable and practical option for sustainable aquaculture feed production.

Revolutionizing Cancer Care: Advances in Carbon-Based Materials for Diagnosis and Treatment.

Cancer involves intricate pathological mechanisms marked by complexities such as cytotoxicity, drug resistance, stem cell proliferation, and inadequate specificity in current chemotherapy approaches. Cancer therapy has embraced diverse nanomaterials renowned for their unique magnetic, electrical, and optical properties to address these challenges. Despite the expanding corpus of knowledge in this area, there has been less advancement in approving nano drugs for use in clinical settings. Nanotechnology, and more especially the development of intelligent nanomaterials, has had a profound impact on cancer research and treatment in recent years. Due to their large surface area, nanoparticles can adeptly encapsulate diverse compounds. Furthermore, the modification of nanoparticles is achievable through a broad spectrum of bio-based substrates, including DNA, aptamers, RNA, and antibodies. This functionalization substantially enhances their theranostic capabilities. Nanomaterials originating from biological sources outperform their conventionally created counterparts, offering advantages such as reduced toxicity, lower manufacturing costs, and enhanced efficiency. This review uses carbon nanomaterials, including graphene-based materials, carbon nanotubes (CNTs) based nanomaterials, and carbon quantum dots (CQDs), to give a complete overview of various methods used in cancer theranostics. We also discussed their advantages and limitations in cancer diagnosis and treatment settings. Carbon nanomaterials might significantly improve cancer theranostics and pave the way for fresh tumor diagnosis and treatment approaches. More study is needed to determine whether using nano-carriers for targeted medicine delivery may increase material utilization. More insight is required to explore the correlation between heightened cytotoxicity and retention resulting from increased permeability.

Evaluating the biological (antidiabetic) potential of TEM, FTIR, XRD, and UV-spectra observed berberis lyceum conjugated silver nanoparticles.

Diabetes is a life-threatening disease that affects different parts of the body including the liver, kidney, and pancreas. The core root of diabetes is mainly linked to oxidative stress produced by reactive oxygen species (ROS). Berberis lyceum Royle (BLR) is the source of natural products. It comprises numerous bioactive compounds having antioxidant activities. In the current investigation, silver nanoparticles from BLR root extract were synthesized, characterized, and assessed for antidiabetic potential. UV spectrophotometry, Transmission electron microscopy (TEM), Fourier transform infra-red spectroscopy (FTIR), and x-ray diffraction (XRD) were applied for the characterization of NPs. It was evident from the morphological studies that the synthesized NPs were spherical and the average size was 11.02 nm. Results revealed that BLR-AgNPs showed higher radical scavenging activity as compared to BLR extract. Moreover, BLR-AgNPs displayed superior in vivo and in vitro antidiabetic activity in comparison to BLR extract. Glucose level (116.5 ± 5.1 mg/dL), liver function test (ALAT: 54.038 ± 6.2 IU/L; ASAT: 104.42 ± 13.9 IU/L; ALP: 192.6 ± 2.4 IU/L; bilirubin: 1.434 ± 0.14 mg/dL; total protein: 5.14 ± 0.24 mg/dL), renal function test (urea: 39.6 ± 0.63 mg/dL; uric acid: 21.4 ± 0.94 mg/dL; creatinine: 0.798 ± 0.03 mg/dL; albumin: 4.14 ± 0.2 mg/dL), lipid profile level (cholesterol: 101.62 ± 3 mg/dL; triglyceride: 110.42 ± 7 mg/dL; HDL-C: 29.7 ± 3 mg/dL; LDL-C: 47.056 ± 1 mg/dL; VLDL-C: 22.0 ± 1.3 mg/dL) and hematology (WBCs: 3.82 ± 0.24 103 /µL; RBCs: 4.78 ± 0.42 106 /µL; Hb: 12.6 ± 1.0 g/dL; Hematocrit: 39.4 ± 3.7%; MCV: 65.8 ± 3 fL; platelets: 312 ± 22.4; neutrophils: 34.8 ± 1.87; eosinophils: 3.08 ± 0.43; monocytes: 3.08 ± 0.28; lymphocytes: 75.6 ± 3.77) confirmed the significant antidiabetic potential of BLR-AgNPs. Histopathological examination authenticated that BLR-AgNPs caused a significant revival in the morphology of the liver, kidney, and pancreas. Hence, findings of the study suggested the BLR-AgNPs as a potent antidiabetic agent and could be an appropriate nanomedicine to prevent diabetes in future. RESEARCH HIGHLIGHTS: Berberis lyceum extract as a reducing, capping, and stabilization agent for the BLR-AgNPs synthesis Evaluation of α-amylase inhibition, antioxidant, and α-glucosidase inhibition potential Thorough characterization using Fourier transform infrared spectroscopy, Transmission electron microscopy, x-ray diffraction, and UV-VIS spectrophotometer, which is 1st of its kind In-vivo antidiabetic activity evaluation through multiple biomarkers.

Downregulation of homeobox B8 in attenuating non-small cell lung cancer cell migration and invasion though the epithelial-mesenchymal transition pathway.

Background: Homeobox (HOX) family genes have been identified as regulators of cancer development. No research exists concerning the mechanisms underlying homeobox B8 (HOXB8) activity in non-small cell lung cancer (NSCLC). In this study, we investigated expression and biological function in NSCLC to determine whether it is an important marker of patient prognosis. Methods: HOXB8 expression in NSCLC tissues was investigated using immunohistochemistry (IHC) and Western blot assays. In addition, HOXB8 was knocked down in NSCLC cells to assess its biological functions in this context. The invasive and migratory potential of cells was evaluated by using Transwell (BD, Franklin Lakes, NJ, USA) inserts with 8-µm pores. Furthermore, Western blotting was used to explore whether HOXB8 can influence epithelial-mesenchymal transition (EMT). Results: HOXB8 was expressed at high levels in NSCLC tissues and cell lines compared with adjacent normal tissues. Patients with high HOXB8 expression had shorter survival time and worse prognosis. HOXB8 expression was associated with pathological grading, tumor size, and lymph node metastasis. HOXB8 was prognostic in patients with NSCLC. After knockdown of HOXB8 via small interfering RNA, the proliferation, migration and invasion ability of the cells were significantly reduced compared with the control group. Moreover, EMT was inhibited by the downregulation of HOXB8 expression, as the expressions of E-cadherin was upregulated and that of the N-cadherin, vimentin, matrix metalloproteinase 2 (MMP2), and twist were downregulated. HOXB8 is a member of the ANTP homeobox family and encodes a nuclear protein with a homeobox DNA-binding domain. It is included in a cluster of homeobox B genes located on chromosome 17. The encoded protein functions as a sequence-specific transcription factor that is involved in development. Conclusions: HOXB8 is highly expressed in NSCLC and may predict prognosis of patients with this type of cancer. Furthermore, HOXB8 may promote NSCLC progression through the regulation of the EMT process.

Identifying key soil characteristics for Francisella tularensis classification with optimized Machine learning models.

Francisella tularensis (Ft) poses a significant threat to both animal and human populations, given its potential as a bioweapon. Current research on the classification of this pathogen and its relationship with soil physical-chemical characteristics often relies on traditional statistical methods. In this study, we leverage advanced machine learning models to enhance the prediction of epidemiological models for soil-based microbes. Our model employs a two-stage feature ranking process to identify crucial soil attributes and hyperparameter optimization for accurate pathogen classification using a unique soil attribute dataset. Optimization involves various classification algorithms, including Support Vector Machines (SVM), Ensemble Models (EM), and Neural Networks (NN), utilizing Bayesian and Random search techniques. Results indicate the significance of soil features such as clay, nitrogen, soluble salts, silt, organic matter, and zinc , while identifying the least significant ones as potassium, calcium, copper, sodium, iron, and phosphorus. Bayesian optimization yields the best results, achieving an accuracy of 86.5% for SVM, 81.8% for EM, and 83.8% for NN. Notably, SVM emerges as the top-performing classifier, with an accuracy of 86.5% for both Bayesian and Random Search optimizations. The insights gained from employing machine learning techniques enhance our understanding of the environmental factors influencing Ft's persistence in soil. This, in turn, reduces the risk of false classifications, contributing to better pandemic control and mitigating socio-economic impacts on communities.