How substituents tune quantum interference in meta-OPE3 molecular junctions to control thermoelectric transport.

Quantum interference (QI) can strongly affect electric and thermoelectric properties of molecular junctions (MJs). So far, however, a limited number of experimental studies have explored the influence of QI on thermoelectric transport in MJs. To address this open point, we synthesized derivatives of meta-OPE3 with an electron-withdrawing nitro (-NO2) substituent or an electron-donating N,N-dimethyl amine (-NMe2) substituent, attached at two different positions of the central phenylene ring, and systematically studied the electrical conductance and thermopower of the corresponding gold-molecule-gold junctions. We show that (i) the electrical conductance of MJs depends weakly on the polarity of the substituents but strongly on the substitution position and (ii) MJs with the N,N-dimethyl amine group feature a higher thermopower than MJs with the nitro group. We also present calculations based on first principles, which explain these trends and show that the transport properties are highly sensitive to microscopic details in junctions, exhibiting destructive QI features.

Study on significance of pillar dimensions on stability of caverns in rocks with high overburden.

The present study aims to examine the influence of pillar widths on the stability of caverns. Case study considering two caverns viz. Powerhouse Cavern (PHC) and Transformerhall Cavern (TC), in a major hydro-electric project in the eastern Himalayas is considered. 2D and 3D numerical analysis was carried out for w/B ratios 1.5 and 3.0 respectively. Primary aspects like major principal stress and development of plastic zone were investigated for the two pillar widths. An optimum pillar width was observed that resulted in reduced stress acting along the cavern periphery, a better stress distribution, and no overlap of plastic zones between the caverns. Further, the optimum pillar width resulted in a better stress-redistribution with progress of excavation and the in-situ stress became constant at an earlier stage of excavation. Observations from comparative analysis revealed that a pillar width nearly equal to the largest dimension or twice the width of the larger of the caverns in the group resulted in a better stability and hence can be considered as the optimum width. Furthermore, the analysis suggests that along the pillar width, maximum stress was observed at mid-height, and it is more in the vicinity of the face of the caverns.

Intensive care unit-acquired weakness and mechanical ventilation: A reciprocal relationship.

Intensive care unit-acquired weakness (ICU-AW; ICD-10 Code: G72.81) is a syndrome of generalized weakness described as clinically detectable weakness in critically ill patients with no other credible cause. The risk factors for ICU-AW include hyperglycemia, parenteral nutrition, vasoactive drugs, neuromuscular blocking agents, corticosteroids, sedatives, some antibiotics, immobilization, the disease severity, septicemia and systemic inflammatory response syndrome, multiorgan failure, prolonged mechanical ventilation (MV), high lactate levels, older age, female sex, and pre-existing systemic morbidities. There is a definite association between the duration of ICU stay and MV with ICU-AW. However, the interpretation that these are modifiable risk factors influencing ICU-AW, appears to be flawed, because the relationship between longer ICU stays and MV with ICU-AW is reciprocal and cannot yield clinically meaningful strategies for the prevention of ICU-AW. Prevention strategies must be based on other risk factors. Large multicentric randomized controlled trials as well as meta-analysis of such studies can be a more useful approach towards determining the influence of these risk factors on the occurrence of ICU-AW in different populations.

Novel automated non-invasive detection of ocular surface squamous neoplasia using artificial intelligence.

Ocular surface squamous neoplasia (OSSN) is a common eye surface tumour, characterized by the growth of abnormal cells on the ocular surface. OSSN includes invasive squamous cell carcinoma (SCC), in which tumour cells penetrate the basement membrane and infiltrate the stroma, as well as non-invasive conjunctival intraepithelial neoplasia, dysplasia, and SCC in-situ thereby presenting a challenge in early detection and diagnosis. Early identification and precise demarcation of the OSSN border leads to straightforward and curative treatments, such as topical medicines, whereas advanced invasive lesions may need orbital exenteration, which carries a risk of death. Artificial intelligence (AI) has emerged as a promising tool in the field of eye care and holds potential for its application in OSSN management. AI algorithms trained on large datasets can analyze ocular surface images to identify suspicious lesions associated with OSSN, aiding ophthalmologists in early detection and diagnosis. AI can also track and monitor lesion progression over time, providing objective measurements to guide treatment decisions. Furthermore, AI can assist in treatment planning by offering personalized recommendations based on patient data and predicting the treatment response. This manuscript highlights the role of AI in OSSN, specifically focusing on its contributions in early detection and diagnosis, assessment of lesion progression, treatment planning, telemedicine and remote monitoring, and research and data analysis.

Intraocular lens selection in diabetic patients: How to increase the odds for success.

The incidence of cataracts is significantly higher in diabetic individuals, particularly in younger age groups, with rates quadrupled in those under 65 and doubled in those over 65 compared to non-diabetics. Cataract surgery in diabetic patients poses many challenges: Poor epithelial healing, decreased corneal sensitivity, increased central corneal thickness, decreased endothelial cell count, variable topography, poor pupillary dilatation, anterior capsular phimosis, posterior capsular opacification (PCO), chances of progression of diabetic retinopathy (DR), zonular weakness, and vitreous prolapse and diabetic macular edema. Selection of an appropriate intraocular lens (IOL) is crucial for visual rehabilitation and monitoring DR. The choice of IOL in diabetic cataract patients is a challenging scenario. Square-edge IOLs are favored for their capacity to mitigate PCO, whereas hydrophilic counterparts may incur calcification in the setting of proliferative DR. The advisability of premium IOLs for achieving spectacle independence warrants judicious evaluation, particularly in the presence of advanced retinopathy. Optimal IOL placement within the capsular bag is advocated to minimize postoperative complications. Rigorous preoperative assessment and informed patient counseling regarding IOL options are indispensable for optimizing surgical outcomes. This review article covers various aspects regarding the choice of IOLs in different case scenarios and complications in the diabetic population.

Retrospective analysis of neoplasms in patients using angiotensin receptor blockers.

In recent years, regulatory agencies have raised concerns about the presence of potentially carcinogenic substances in certain formulations of Angiotensin Receptor Blockers (ARBs). Specifically, nitrosamines and azido compounds have been identified in some ARB products. Nitrosamines are known to have carcinogenic properties and are associated with an increased risk of neoplasms. Spontaneous safety reports from the EudraVigilance Data Analysis System (EVDAS) database were analyzed to investigate cases of neoplasms associated with ARBs. A disproportionality analysis was conducted, calculating the reporting odds ratio (ROR) and 95% confidence intervals (CIs) using a case/non-case approach for each ARB drug. The EVDAS database contained 68,522 safety reports related to ARBs (including Azilsartan, Candesartan, Irbesartan, Olmesartan, Losartan, Valsartan, and Telmisartan), among which 3,396 (5%) cases were associated with neoplasms. The majority of these cases were reported in Germany (11.9%), followed by France (9.7%). Approximately 70% of the reports were submitted by healthcare professionals such as physicians and nurses. Among the ARBs, valsartan had the highest ROR for neoplasm (ROR 1.949, 95% CI 1.857-2.046). This association remained significant when comparing ARBs with other classes of antihypertensive drugs, including ACE inhibitors, beta-blockers, calcium channel blockers, and diuretics. Our study identifies a possible signal of an association between ARBs, particularly valsartan, and the risk of neoplasms. However, further observational and analytical studies are necessary to confirm these findings and elucidate the underlying mechanisms.

PMTPred: machine-learning-based prediction of protein methyltransferases using the composition of k-spaced amino acid pairs.

Protein methyltransferases (PMTs) are a group of enzymes that help catalyze the transfer of a methyl group to its substrates. These enzymes play an important role in epigenetic regulation and can methylate various substrates with DNA, RNA, protein, and small-molecule secondary metabolites. Dysregulation of methyltransferases is implicated in various human cancers. However, in light of the well-recognized significance of PMTs, reliable and efficient identification methods are essential. In the present work, we propose a machine-learning-based method for the identification of PMTs. Various sequence-based features were calculated, and prediction models were trained using various machine-learning algorithms using a tenfold cross-validation technique. After evaluating each model on the dataset, the SVM-based CKSAAP model achieved the highest prediction accuracy with balanced sensitivity and specificity. Also, this SVM model outperformed deep-learning algorithms for the prediction of PMTs. In addition, cross-database validation was performed to ensure the robustness of the model. Feature importance was assessed using shapley additive explanations (SHAP) values, providing insights into the contributions of different features to the model's predictions. Finally, the SVM-based CKSAAP model was implemented in a standalone tool, PMTPred, due to its consistent performance during independent testing and cross-database evaluation. We believe that PMTPred will be a useful and efficient tool for the identification of PMTs. The PMTPred is freely available for download at https://github.com/ArvindYadav7/PMTPred and http://www.bioinfoindia.org/PMTPred/home.html for research and academic use.

Bio-inspired multifunctional and reusable LiP@MFO-GO and LiP@MFO-Chit hybrid enzyme complexes for efficient degradation of melanin.

Melanin is a complex brown pigment, primarily responsible for the skin pigmentation. Therefore, cosmetic industries have always been in search of potent oxidative enzymes useful for melanin degradation, and to promise a fair complexion after using their products. In the present study, lignin peroxidase from Pseudomonas fluorescence LiP-RL5 isolate has been immobilized on super-paramagnetic nanoparticles to enhance its stability and reusability. The chitosan coated enzyme-nanomaterial complex (LiP@MFO-Chit) showed higher melanin decolorization (47.30 ± 2.3 %) compared to the graphene oxide coated nanoparticles (LiP@MFO-GO) (41.60 ± 1.6 %). Synthesized enzyme nanoparticle complexes showed microbicidal effect on skin infection causing pathogen, Pantoea agglomerans with an inhibitory zone of 6.0 ± 0.9 mm and 250 µg/100 µl minimum inhibitory concentration, and a 7.0 ± 1.5 mm zone and 170 µg/100 µl MIC for LiP@MFO-GO and LiP@MFO-Chit, respectively. Antioxidant potential of LiP@MFO-Chit and LiP@MFO-GO nano-conjugates showed a substantial DPPH scavenging activity of 75.7 % and 88.3 %, respectively. Therefore, LiP-nanoparticle hybrid complexes analyzed in this study are not only effective as skin whitening agents but they are potential molecules against various microbial skin infections as well as useful for different other biomedical applications like biorefinery, drug delivery, and dermatology, etc.

Air pollution mitigation and suspended particulate matter retention potential of selected plant species across seasonal variation in the urban area.

Air pollution is a pressing environmental concern in urban areas, especially in densely populated cities like Delhi, India. However, plant species can effectively capture airborne suspended pollutants. Given this, the present study aimed to investigate the seasonal variations (pre- and post-monsoon) in the pollution-mitigating potential, biochemical characteristics, and suspended particulate matter (SPM) capturing capacities of select plant species in Delhi. Also, using biochemical parameters, plant morphology, and socioeconomic factors, the study computed tolerance indices such as the Air Pollution Tolerance Index (APTI) and Anticipated Performance Index (API). Ficus religiosa L. exhibited the highest APTI value of 11.94, while Polyalthia longifolia (Sonn.) Thwaites displayed the lowest 7.99 APTI value during the pre-monsoon. Ficus benghalensis L. showed the maximum SPM adhesion on the leaves, with a deposition of 1305.46 µg/cm2, whereas F. religiosa exhibited the lowest SPM deposition of 56.62 µg/cm2. Moreover, the statistical analysis indicated a positive correlation between ascorbic acid and chlorophyll content (R2 > 0.6) with APTI. Also, F. religiosa demonstrated a significant Pearson's correlation (P < 0.05) between chlorophyll content and SPM deposition during the pre-monsoon. The study highlighted the dynamic nature of plant-based air pollution mitigation. It offered valuable insights into the potential of green infrastructure as a sustainable solution for addressing air quality concerns in urban environments. The results emphasized the significance of selecting adequate plant species and considering seasonal variations in developing urban greening strategies to combat air pollution.

Ultra-wide range non-contact surface profilometry based on reconfigurable fiber interferometry.

Surface characterization is essential for a technical evaluation of device performance and to assess surface dynamics in fabrication units. In this regard, a number of surface profiling techniques have been developed that accurately map sample topography but have significantly limited detection range. Here, we demonstrate a cascaded non-contact fiber interferometer-based approach for real-time high-precision surface profiling with ultrawide detection range (nm to mm). This compact interferometers' system operates by wavelength interrogation that provides a scope to study several types of surfaces and has a tunable cavity configuration for varying the sensitivity and range of the detectable features' size. The proposed system enables nanoscale profiling over 10-1000 nm with resolution of 10 nm and microscale mapping over 1-1000 µm with resolution of 0.2 µm. The technique is utilized to map the features of nanostructured surfaces and estimate the surface roughness of standardized industrial samples.

An Image-Guided Percutaneous Core Needle Biopsy for Accurate Assessment of Musculoskeletal Tumors: A Targeted Diagnosis.

Background Accurate diagnosis of musculoskeletal tumors is essential for guiding appropriate treatment strategies. Percutaneous core needle biopsy (PCNB) is increasingly recognized as a valuable method for obtaining tissue samples for histopathological examination. This study aims to evaluate the diagnostic accuracy and clinical utility of PCNB in diagnosing musculoskeletal tumors. Methodology A total of 152 cases suspected of musculoskeletal tumors underwent PCNB at our tertiary care center between 2020 and 2023. Pre-biopsy evaluation included comprehensive clinical assessment and imaging studies. Core biopsies were performed under image guidance, with specimens sent for histopathological examination and culture sensitivity analysis. Diagnostic yield, accuracy, and performance metrics of PCNB were assessed. Results PCNB demonstrated a diagnostic yield of 93.4%. However, in cases where initial biopsies were inconclusive, repeat core biopsy or open biopsy provided the necessary diagnostic clarity. PCNB demonstrated a remarkable diagnostic accuracy of 97.9%, with a specificity and positive predictive value of 100%. There were no post-biopsy complications and no instances of local recurrence from the biopsy tract. Conclusions PCNB can be a reliable method for diagnosing musculoskeletal tumors, offering high diagnostic accuracy and minimal complications. The utilization of image guidance enhances precision and reduces the risk of complications. PCNB proves effective in diagnosing both primary tumors and bone infections, facilitating timely and appropriate treatment strategies in orthopedic oncology.

Assessment of metals and metalloids agglutinated to airborne suspended particulate matter in selected plant species during the pre-and post-monsoon in the urban area.

The elemental accumulation has emerged as a major environmental concern due to various anthropogenic sources such as vehicles, road dust, and industrial activities, contributing to the agglutination of elements to airborne Suspended Particulate Matter (SPM). SPM-bound elements accumulate on plant surfaces impact air quality and human health due to their noxiousness. Therefore, plants' ability to capture and mitigate air pollutants plays a crucial role in urban areas. This study aimed to investigate the levels and distribution of twenty-six elements, comprised of heavy metals (Cd, Pb, Cr, Cu Zn, Co, Ni, Fe, Mn, Ag, Mo, V, Ga, and Bi), light metals (B, As, Te, and Se), and metalloids (Al, Li, Sr, K, Mg, Na, Ca, and Ba) accumulated on the surface and inside the leaves of dominant plant species during the pre-and post-monsoon at six categorized (commercial, traffic-prone, residential, educational, greenbelt and industrial areas) locations in Delhi, India. In addition, the Metal Accumulation Index (MAI) was determined, and the statistical analysis was conducted using two-way ANOVA, Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA). In the pre-and post-monsoon, two-way ANOVA revealed significant differences (P < 0.05) in metal concentrations. During the pre-monsoon plants exhibited the highest metal accumulation (∼21%) at the Anand Vihar (commercial) in Delhi, with the maximum average concentrations of Cr (118.25 mg/kg), Cu (204.38 mg/kg), Zn (293.27 mg/kg), and Fe (2721.17 mg/kg). Ficus benghalensis L exhibited the maximum 213.73 MAI at the Anand Vihar in the pre-monsoon. Ni and Cr indicated the highest correlation (P < 0.05, r = 0.82) in the PCA test. HCA test revealed similarity (∼87.7%) at ITO (traffic-prone) and Okhla Phase-2 (industrial) in F. religiosa regarding metal concentration patterns. Findings highlighted seasonal elemental pollutants uptake dynamics of plant species and explored species-specific metal accumulation, revealing potential implications of metal-tolerant plants for urban greenbelt.

PCA and fuzzy clustering-based delineation of soil nutrient (S, B, Zn, Mn, Fe, and Cu) management zones of sub-tropical Northeastern India for precision nutrient management.

Understanding the spatial distribution of plant available soil nutrients and influencing soil properties and delineation soil nutrient management zones (MZs) are important for implementing precision nutrient management options (PNMO) in an area to achieve maintainable crop production. We assessed spatial distribution pattern of plant available sulphur (S) (PAS), boron (B) (PAB), zinc (PAZn), manganese (PAMn), iron (PAFe), and copper (PACu), and soil organic carbon (SOC), pH, and electrical conductivity (EC) to delineate soil nutrients MZs in northeastern region of India. A total of 17,471 representative surface (0-15 cm depth) soil samples were collected from the region, processed, and analysed for above-mentioned soil parameters. The values of PAS (0.22-99.2 mg kg-1), PAB (0.01-6.45 mg kg-1), PAZn (0.05-13.9 mg kg-1), PAMn (0.08-158 mg kg-1), PAFe (0.50-472 mg kg-1), PACu (0.01-19.2 mg kg-1), SOC (0.01-5.80%), pH (3.19-7.56) and EC (0.01-1.66 dS m-1) varied widely with coefficient of variation of 15.5-108%. The semivariogram analysis highlighted exponential, Gaussian and stable best fitted models for soil parameters with weak (PACu), moderate (PAB, PAZn, PAFe, SOC, pH, and EC) and strong (PAS, and PAMn) spatial dependence. The ordinary kriging interpolation revealed different distribution patterns of soil parameters. About 14.8, 27.5, and 3.40% area of the region had PAS of ≤15.0 mg kg-1, PAB of ≤0.50 mg kg-1, and PAZn of had ≤0.90 mg kg-1, respectively. About 67.5, and 32.5% area had SOC content >1.00 and < 1.00%, respectively. Soil pH was ≤5.50, and >5.50 to ≤6.50 in 41.7 and 40.3% area of the region, respectively. The techniques of principal component analysis and fuzzy c-mean algorithm clustering produced 6 MZs of the region with different areas and values of soil parameters. The MZs had different levels of deficiency pertaining to PAS, PAB, and PAZn. The produced MZ maps could be used for managing PAS, PAB, PAZn, SOC and soil pH in order to implement PNMO. The study highlighted the usefulness of MZ delineation technique for implementation of PNMO in different cultivated areas for sustainable crop production.

Role of Repetitive Transcranial Magnetic Stimulation in Treatment of Fibromyalgia: A Randomized Controlled Trial.

Background and Objective: Fibromyalgia syndrome (FMS) is a chronic disease characterized by widespread, persistent musculoskeletal pain in association with impaired health-related quality of life. Repetitive transcranial magnetic stimulation (rTMS) is an emerging tool for the management of fibromyalgia. There is no standardized protocol of rTMS for the treatment of FMS, and both low- and high-frequency stimulation of the dorsolateral prefrontal cortex (DLPFC) are described in the literature with variable efficacy. The objective of this study was to determine the effectiveness of rTMS in people with fibromyalgia and compare the response of low- and high-frequency stimulation with sham stimulation. Materials and Methods: This study was a single-blinded, randomized, placebo-controlled trial. Ninety patients with the diagnosis of FMS were randomly allocated into one of the following three groups: low-frequency (1 Hz) group, high-frequency (10 Hz) group, and sham group. Pain, depression, anxiety, and quality of life were measured using the Numerical Pain Rating Scale (NPRS), Hamilton Anxiety Rating Scale (HAM-A), Hamilton Depression Rating Scale (HDRS), and Revised Fibromyalgia Impact Questionnaire (FIQR) immediately following treatment as well as at 1 and 3 months after treatment. The data was statistically analyzed using Statistical Package for the Social Sciences version 23 software. P value < 0.05 was considered statistically significant. Results: Intergroup analysis revealed a significant improvement in NPRS, HAM-A, HDRS, and FIQR scores in both low- and high- frequency groups immediately following treatment and for 3 months after treatment. No significant difference in the efficacy of low- and high-frequency stimulation was noticed. Conclusions: rTMS is an effective mode of treatment in people with FMS. Both low and high frequencies of stimulation at DLPFC are equally effective in reducing pain and associated symptoms.

A mathematical framework for analysing particle flow in a network with multiple pools.

In many real-world systems, the entry rate of particles into a lane is affected by the occupancy of nearby pools. For instance, in biological networks, the concentration of molecules on the side of a membrane affects the entry of particles through the membrane. To understand the behaviour of such networks, we develop a network model of ribosome flow models (RFMs) having multiple pools where each RFM captures the dynamics of particle flow in a lane and competes for the finite resources present at the nearby pool. We study a ribosome flow model network with two pools (RFMNTP) and show that the network always admits a steady state. We then analyse the behaviour of the RFMNTP with respect to modifying the transition rate through a theoretical framework. Simulations of the RFMNTP demonstrate a counterintuitive result. For example, increasing any of the transition rates in the presence of a slow site in an RFM can increase the output rate of some RFMs and decrease the output rate of the other RFMs simultaneously. This suggests that the role of local sharing of particles incorporated is non-trivial. Finally, we illustrate how these results can provide insights into studying a network with multiple pools.

Guardians of the Gut: Harnessing the Power of Probiotic Microbiota and Their Exopolysaccharides to Mitigate Heavy Metal Toxicity in Human for Better Health.

Heavy metal pollution is a significant global health concern, posing risks to both the environment and human health. Exposure to heavy metals happens through various channels like contaminated water, food, air, and workplaces, resulting in severe health implications. Heavy metals also disrupt the gut's microbial balance, leading to dysbiosis characterized by a decrease in beneficial microorganisms and proliferation in harmful ones, ultimately exacerbating health problems. Probiotic microorganisms have demonstrated their ability to adsorb and sequester heavy metals, while their exopolysaccharides (EPS) exhibit chelating properties, aiding in mitigating heavy metal toxicity. These beneficial microorganisms aid in restoring gut integrity through processes like biosorption, bioaccumulation, and biotransformation of heavy metals. Incorporating probiotic strains with high affinity for heavy metals into functional foods and supplements presents a practical approach to mitigating heavy metal toxicity while enhancing gut health. Utilizing probiotic microbiota and their exopolysaccharides to address heavy metal toxicity offers a novel method for improving human health through modulation of the gut microbiome. By combining probiotics and exopolysaccharides, a distinctive strategy emerges for mitigating heavy metal toxicity, highlighting promising avenues for therapeutic interventions and health improvements. Further exploration in this domain could lead to groundbreaking therapies and preventive measures, underscoring probiotic microbiota and exopolysaccharides as natural and environmentally friendly solutions to heavy metal toxicity. This, in turn, could enhance public health by safeguarding the gut from environmental contaminants.

Characterization of Alternaria blotch disease of apple in Himachal Pradesh, India: insights on morphology, pathogenicity, and molecular features.

BACKGROUND: Alternaria blotch disease in Himachal Pradesh, India, caused by Alternaria spp., adversely affects apple cultivars, resulting in reduced fruit size and quality accompanied by premature leaf fall. METHODS AND RESULTS: Sixteen Alternaria isolates from apple growing regions underwent comprehensive analysis including morphology, pathogenicity, and molecular characterization. Variations in conidiophore and conidia dimensions, shapes, and divisions were observed among isolates. Pathogenicity assays revealed differences in incubation periods, latent phases, and disease responses. Molecular characterization via nuclear ITS rDNA and RAPD analysis indicated 99-100% homology with Alternaria alternata, Alternaria mali, and other Alternaria spp., with a close phylogenetic relationship to Chinese isolates. Differentiation of isolates based on origin, cultural characteristics, and morphology was achieved using RAPD markers. CONCLUSIONS: The study identifies diverse genotypes and morphotypes of Alternaria contributing to apple blotch disease in Himachal Pradesh. These findings highlight the complexity of the pathogenic environment and hold significant implications for disease management in apple orchards.

Advancement in Cancer Vasculogenesis Modeling through 3D Bioprinting Technology.

Cancer vasculogenesis is a pivotal focus of cancer research and treatment given its critical role in tumor development, metastasis, and the formation of vasculogenic microenvironments. Traditional approaches to investigating cancer vasculogenesis face significant challenges in accurately modeling intricate microenvironments. Recent advancements in three-dimensional (3D) bioprinting technology present promising solutions to these challenges. This review provides an overview of cancer vasculogenesis and underscores the importance of precise modeling. It juxtaposes traditional techniques with 3D bioprinting technologies, elucidating the advantages of the latter in developing cancer vasculogenesis models. Furthermore, it explores applications in pathological investigations, preclinical medication screening for personalized treatment and cancer diagnostics, and envisages future prospects for 3D bioprinted cancer vasculogenesis models. Despite notable advancements, current 3D bioprinting techniques for cancer vasculogenesis modeling have several limitations. Nonetheless, by overcoming these challenges and with technological advances, 3D bioprinting exhibits immense potential for revolutionizing the understanding of cancer vasculogenesis and augmenting treatment modalities.

Occurrence of Sphingomonas olei with elemental S oxidation capability in sodic soil: Potential role in sodicity reclamation and plant growth promotion.

The success of the sodic soil reclamation using elemental S (S°) depends on the population of the native S° oxidizers. Augmenting the native flora of the sodic soils with effective S° oxidizers can enhance the success of the sodic soil reclamation. Present study reports for the first time the S° oxidation potential of the Sphingomonas olei strain 20UP7 isolated from sodic soils with pHs 9.8 and ECe 3.6 dS m-1. Inoculation with S. olei strain 20UP7 caused 13.0-24.2 % increase in S° oxidation in different sodic soils (pHs 9.1-10.5). It improved the concentration of the Ca2+, Mg2+, PO43- and declined the HCO3- and total alkalinity of the soil solution. This isolate also showed appreciable P and Zn solubilization, indole acetic acid, ammonia, and titratable acidity production in the growth media. It tended to the formation of biofilm around sulphur particles. The PCR amplification with gene-specific primers showed the occurrence of soxA, soxB, and soxY genes with a single band corresponding to length of 850, 460, and 360 base pairs, respectively. The integration of the S. olei strain 20UP7 with S° caused 21.7-25.4 % increase in the rice and wheat yield compared to the soil treated with S° alone. This study concludes that the S. olei, native to high saline-sodic soils can be utilized for improving the sodicity reclamation and plant growth promotion using elemental S based formulations.